PUBLICATIONS

2024

1. 

   COLLABORATING FOUNDATION MODELS FOR DOMAIN GENERALIZED SEMANTIC SEGMENTATION

   Y. Benigmim , S. Roy , S. Essid, V. Kalogeiton , and S. Lathuilière

   In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024) , 2024

   Accepted

   Abs Bib PDF

   Domain Generalized Semantic Segmentation (DGSS) deals with training a model on a labeled source domain with the aim of generalizing to unseen domains during inference. Existing DGSS methods typically effectuate robust features by means of Domain Randomization (DR). Such an approach is often limited as it can only account for style diversification and not content. In this work, we take an orthogonal approach to DGSS and propose to use an assembly of CoLlaborative FOUndation models for Domain Generalized Semantic Segmentation (CLOUDS). In detail, CLOUDS is a framework that integrates FMs of various kinds: (i) CLIP backbone for its robust feature representation, (ii) generative models to diversify the content, thereby covering various modes of the possible target distribution, and (iii) Segment Anything Model (SAM) for iteratively refining the predictions of the segmentation model. Extensive experiments show that our CLOUDS excels in adapting from synthetic to real DGSS benchmarks and under varying weather conditions, notably outperforming prior methods by 5.6% and 6.7% on averaged miou, respectively.

   @inproceedings{benigmim2023collaborating,
     title = {Collaborating Foundation models for Domain Generalized Semantic Segmentation},
     author = {Benigmim, Yasser and Roy, Subhankar and Essid, Slim and Kalogeiton, Vicky and Lathuilière, Stéphane},
     booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2024)},
     year = {2024},
     eprint = {2312.09788},
     archiveprefix = {arXiv},
     primaryclass = {cs.CV},
     note = {Accepted},
   }

2. 

   SELF-SUPERVISED LEARNING OF MULTI-LEVEL AUDIO REPRESENTATIONS FOR MUSIC SEGMENTATION

   M. Buisson , B. Mcfee , S. Essid, and H. Crayencour

   IEEE/ACM Transactions on Audio, Speech and Language Processing, Mar 2024

   Accepted

   Abs Bib PDF

   The task of music structure analysis refers to automatically identifying the location and the nature of musical sections within a song. In the supervised scenario, structural annotations generally result from exhaustive data collection processes, which represents one of the main challenges of this task. Moreover, both the subjectivity of music structure and the hierarchical characteristics it exhibits make the obtained structural annotations not fully reliable, in the sense that they do not convey a "universal ground-truth" unlike other tasks in music information retrieval. On the other hand, the quickly growing quantity of available music data has enabled weakly supervised and self-supervised approaches to achieve impressive results on a wide range of music-related problems. In this work, a self-supervised learning method is proposed to learn robust multi-level music representations prior to structural segmentation using contrastive learning. To this end, sets of frames sampled at different levels of detail are used to train a deep neural network in a disentangled manner. The proposed method is evaluated on both flat and multi-level segmentation. We show that each distinct sub-region of the output embeddings can efficiently account for structural similarity at their own targeted level of detail, which ultimately improves performance of downstream flat and multi-level segmentation. Finally, complementary experiments are carried out to study how the obtained representations can be further adapted to specific datasets using a supervised fine-tuning objective in order to facilitate structure retrieval in domains where human annotations remain scarce.

   @article{buisson:hal-04485065,
     title = {{Self-Supervised Learning of Multi-level Audio Representations for Music Segmentation}},
     author = {Buisson, Morgan and Mcfee, Brian and Essid, Slim and Crayencour, Helene-Camille},
     journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing},
     url = {https://hal.science/hal-04485065},
     note = {Accepted},
     year = {2024},
     month = mar,
     keywords = {Music structure analysis ; structural segmentation ; representation learning},
     hal_version = {v1},
   }

3. ON THE CHOICE OF THE OPTIMAL TEMPORAL SUPPORT FOR AUDIO CLASSIFICATION WITH PRE-TRAINED EMBEDDINGS

   A. Quelennec , M. Olvera , G. Peeters , and S. Essid

   In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024) , Apr 2024

   Accepted

   Abs Bib PDF

   Current state-of-the-art audio analysis systems rely on pre-trained embedding models, often used off-the-shelf as (frozen) feature extractors. Choosing the best one for a set of tasks is the subject of many recent publications. However, one aspect often overlooked in these works is the influence of the duration of audio input considered to extract an embedding, which we refer to as Temporal Support (TS). In this work, we study the influence of the TS for well-established or emerging pre-trained embeddings, chosen to represent different types of architectures and learning paradigms. We conduct this evaluation using both musical instrument and environmental sound datasets, namely OpenMIC, TAU Urban Acoustic Scenes 2020 Mobile, and ESC-50. We especially highlight that Audio Spectrogram Transformer-based systems (PaSST and BEATs) remain effective with smaller TS, which therefore allows for a drastic reduction in memory and computational cost. Moreover, we show that by choosing the optimal TS we reach competitive results across all tasks. In particular, we improve the state-of-the-art results on OpenMIC, using BEATs and PaSST without any fine-tuning.

   @inproceedings{quelennec:hal-04360221,
     title = {{On the Choice of the Optimal Temporal Support for Audio Classification with Pre-trained Embeddings}},
     author = {Quelennec, Aurian and Olvera, Michel and Peeters, Geoffroy and Essid, Slim},
     url = {https://hal.science/hal-04360221},
     booktitle = {{IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)}},
     year = {2024},
     note = {Accepted},
     month = apr,
     keywords = {audio embeddings ; acoustic scene classification ; instrument recognition ; temporal support ; transformers ; Representation Model},
     hal_id = {hal-04360221},
     hal_version = {v1}
   }

4. ONLINE SPEAKER DIARIZATION OF MEETINGS GUIDED BY SPEECH SEPARATION

   E. Gruttadauria , M. Fontaine , and S. Essid

   In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024) , Apr 2024

   Accepted

   Abs Bib PDF

   Overlapped speech is notoriously problematic for speaker diarization systems. Consequently, the use of speech separation has recently been proposed to improve their performance. Although promising, speech separation models struggle with realistic data because they are trained on simulated mixtures with a fixed number of speakers. In this work, we introduce a new speech separation-guided diarization scheme suitable for the online speaker diarization of long meeting recordings with a variable number of speakers, as present in the AMI corpus. We envisage ConvTasNet and DPRNN as alternatives for the separation networks, with two or three output sources. To obtain the speaker diarization result, voice activity detection is applied on each estimated source. The final model is fine-tuned end-to-end, after first adapting the separation to real data using AMI. The system operates on short segments, and inference is performed by stitching the local predictions using speaker embeddings and incremental clustering. The results show that our system improves the state-of-the-art on the AMI headset mix, using no oracle information and under full evaluation (no collar and including overlapped speech). Finally, we show the strength of our system particularly on overlapped speech sections.

   @inproceedings{gruttadauria:hal-04419041,
     title = {{Online Speaker Diarization of Meetings Guided by Speech Separation}},
     author = {Gruttadauria, Elio and Fontaine, Mathieu and Essid, Slim},
     url = {https://hal.science/hal-04419041},
     note = {Accepted},
     booktitle = {{IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2024)}},
     address = {Seoul (Korea), South Korea},
     year = {2024},
     month = apr,
     keywords = {Speaker Diarization ; Source separation ; Online inference ; Overlapped speech ; AMI dataset ; Speaker embedding},
     hal_id = {hal-04419041},
     hal_version = {v1}
   }

2023

1. A REPETITION-BASED TRIPLET MINING APPROACH FOR MUSIC SEGMENTATION

   M. Buisson , B. McFee , S. Essid, and H. Crayencour

   In International Society for Music Information Retrieval (ISMIR) , Nov 2023

   Abs Bib PDF

   Contrastive learning has recently appeared as a well-suited method to find representations of music audio signals that are suitable for structural segmentation. However, most existing unsupervised training strategies omit the notion of repetition and therefore fail at encompassing this essential aspect of music structure. This work introduces a triplet mining method which explicitly considers repeating sequences occurring inside a music track by leveraging common audio descriptors. We study its impact on the learned representations through downstream music segmentation. Because musical repetitions can be of different natures, we give further insight on the role of the audio descriptors employed at the triplet mining stage as well as the trade-off existing between the quality of the triplets mined and the quantity of unlabelled data used for training. We observe that our method requires less non-annotated data while remaining competitive against other unsupervised methods trained on a larger corpus.

   @inproceedings{buisson:ismir23,
     title = {A Repetition-based Triplet Mining Approach for Music Segmentation},
     author = {Buisson, Morgan and McFee, Brian and Essid, Slim and Crayencour, Hélène C.},
     booktitle = {International Society for Music Information Retrieval (ISMIR)},
     address = {Milano, Italy},
     year = {2023},
     month = nov,
   }

2. 

   RESILIENT MULTIPLE CHOICE LEARNING: A LEARNED SCORING SCHEME WITH APPLICATION TO AUDIO SCENE ANALYSIS

   V. Letzelter , M. Fontaine , P. Perez , G. Richard , S. Essid, and M. Chen

   In Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023) , Dec 2023

   Abs Bib PDF

   We introduce Resilient Multiple Choice Learning (rMCL), an extension of the MCL approach for conditional distribution estimation in regression settings where multiple targets may be sampled for each training input. Multiple Choice Learning is a simple framework to tackle multimodal density estimation, using the Winner-Takes-All (WTA) loss for a set of hypotheses. In regression settings, the existing MCL variants focus on merging the hypotheses, thereby eventually sacrificing the diversity of the predictions. In contrast, our method relies on a novel learned scoring scheme underpinned by a mathematical framework based on Voronoi tessellations of the output space, from which we can derive a probabilistic interpretation. After empirically validating rMCL with experiments on synthetic data, we further assess its merits on the sound source localization problem, demonstrating its practical usefulness and the relevance of its interpretation.

   @inproceedings{letzelter:hal-04216055,
     title = {Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysis},
     author = {Letzelter, Victor and Fontaine, Mathieu and Perez, Patrick and Richard, Gael and Essid, Slim and Chen, Mickael},
     url = {https://hal.science/hal-04216055},
     booktitle = {Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)},
     address = {New Orleans, United States},
     year = {2023},
     month = dec,
     hal_id = {hal-04216055},
     hal_version = {v1},
   }

3. SPEECH SELF-SUPERVISED REPRESENTATION BENCHMARKING: ARE WE DOING IT RIGHT?

   S. Zaiem , T. Parcollet , and S. Essid

   In Interspeech , Aug 2023

   Bib PDF

   @inproceedings{zaiem:interspeech23,
     title = {Speech Self-Supervised Representation Benchmarking: Are We Doing it Right?},
     author = {Zaiem, Salah and Parcollet, Titouan and Essid, Slim},
     booktitle = {Interspeech},
     address = {Dublin, Ireland},
     year = {2023},
     month = aug,
   }

4. AUTOMATIC DATA AUGMENTATION FOR DOMAIN ADAPTED FINE-TUNING OF SELF-SUPERVISED SPEECH REPRESENTATIONS

   S. Zaiem , T. Parcollet , and S. Essid

   In Interspeech , Aug 2023

   Abs Bib PDF

   Self-Supervised Learning (SSL) has allowed leveraging large amounts of unlabeled speech data to improve the performance of speech recognition models even with small annotated datasets. Despite this, speech SSL representations may fail while facing an acoustic mismatch between the pretraining and target datasets. To address this issue, we propose a novel supervised domain adaptation method, designed for cases exhibiting such a mismatch in acoustic domains. It consists in applying properly calibrated data augmentations on a large clean dataset, bringing it closer to the target domain, and using it as part of an initial fine-tuning stage. Augmentations are automatically selected through the minimization of a conditional-dependence estimator, based on the target dataset. The approach is validated during an oracle experiment with controlled distortions and on two amateur-collected low-resource domains, reaching better performances compared to the baselines in both cases.

   @inproceedings{zaiem:interspeech23b,
     title = {Automatic Data Augmentation for Domain Adapted Fine-Tuning of Self-Supervised Speech Representations},
     author = {Zaiem, Salah and Parcollet, Titouan and Essid, Slim},
     booktitle = {Interspeech},
     address = {Dublin, Ireland},
     year = {2023},
     month = aug,
   }

5. ONE-SHOT UNSUPERVISED DOMAIN ADAPTATION WITH PERSONALIZED DIFFUSION MODELS

   Y. Benigmim , S. Roy , S. Essid, V. Kalogeiton , and S. Lathuiliere

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , Jun 2023

   Abs Bib

   Adapting a segmentation model from a labeled source domain to a target domain, where a single unlabeled datum is available, is one the most challenging problems in domain adaptation and is otherwise known as one-shot unsupervised domain adaptation (OSUDA). Most of the prior works have addressed the problem by relying on style transfer techniques, where the source images are stylized to have the appearance of the target domain. Departing from the common notion of transferring only the target “texture” information, we leverage text-to-image diffusion models (e.g., Stable Diffusion) to generate a synthetic target dataset with photo-realistic images that not only faithfully depict the style of the target domain, but are also characterized by novel scenes in diverse contexts. The text interface in our method Data AugmenTation with diffUsion Models (DATUM) endows us with the possibility of guiding the generation of images towards desired semantic concepts while respecting the original spatial context of a single training image, which is not possible in existing OSUDA methods. Extensive experiments on standard benchmarks show that our DATUM surpasses the state-of-the-art OSUDA methods by up to +7.1%. The implementation is available at https://github.com/yasserben/DATUM

   @inproceedings{Benigmim_2023_CVPR,
     author = {Benigmim, Yasser and Roy, Subhankar and Essid, Slim and Kalogeiton, Vicky and Lathuiliere, Stephane},
     title = {One-Shot Unsupervised Domain Adaptation With Personalized Diffusion Models},
     booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
     month = jun,
     year = {2023},
     pages = {698-708}
   }

6. COSMOPOLITE SOUND MONITORING (COSMO): A STUDY OF URBAN SOUND EVENT DETECTION SYSTEMS GENERALIZING TO MULTIPLE CITIES

   F. Angulo , S. Essid, G. Peeters , and C. Mietlicki

   In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Jun 2023

   Bib

   @inproceedings{10095833,
     author = {Angulo, Florian and Essid, Slim and Peeters, Geoffroy and Mietlicki, Christophe},
     booktitle = {ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
     title = {Cosmopolite Sound Monitoring (CoSMo): A Study of Urban Sound Event Detection Systems Generalizing to Multiple Cities},
     year = {2023},
     pages = {1-5},
     doi = {10.1109/ICASSP49357.2023.10095833},
     abstrat = {
         Measuring noise in cities and automatically identifying the corresponding sound sources are a crucial challenge for policymakers. Indeed, such information helps addressing noise pollution and improving the well-being of urban dwellers. In recent years, researchers have provided annotated datasets recorded in two major cities to foster the development of urban sound event detection (SED) systems. This paper presents an in-depth study of the behaviour of state-of-the-art SED systems well suited to our problem, combining three far-field real recordings datasets which can be used jointly during training. In our evaluation, we highlight the performance gaps existing between simple and hard recording examples based on the salience of sound events and the polyphony of the recordings. We provide new proximity annotations for this analysis. We evaluate the ability of urban SED systems to generalize across cities with varying degrees of training supervision. We show that such generalization is hindered mostly by the difficulties current urban SED systems have to detect sound events with low salience along with sound events in highly polyphonic soundscapes.}
   }

7. FINE-TUNING STRATEGIES FOR FASTER INFERENCE USING SPEECH SELF-SUPERVISED MODELS: A COMPARATIVE STUDY

   S. Zaiem , R. Algayres , T. Parcollet , S. Essid, and M. Ravanelli

   In ICASSP 2023 - International Conference on Acoustics, Speech, and Signal Processing , Jun 2023

   Abs Bib PDF

   Self-supervised learning (SSL) has allowed substantial progress in Automatic Speech Recognition (ASR) performance in low-resource settings. In this context, it has been demonstrated that larger selfsupervised feature extractors are crucial for achieving lower downstream ASR error rates. Thus, better performance might be sanctioned with longer inferences. This article explores different approaches that may be deployed during the fine-tuning to reduce the computations needed in the SSL encoder, leading to faster inferences. We adapt a number of existing techniques to common ASR settings and benchmark them, displaying performance drops and gains in inference times. Interestingly, we found that given enough downstream data, a simple downsampling of the input sequences outperforms the other methods with both low performance drops and high computational savings, reducing computations by 61.3% with an WER increase of only 0.81. Finally, we analyze the robustness of the comparison to changes in dataset conditions, revealing sensitivity to dataset size.

   @inproceedings{zaiem:hal-04076307,
     title = {Fine-tuning strategies for faster inference using speech self-supervised models: a comparative study},
     author = {Zaiem, Salah and Algayres, Robin and Parcollet, Titouan and Essid, Slim and Ravanelli, Mirco},
     url = {https://hal.science/hal-04076307},
     booktitle = {ICASSP 2023 - International Conference on Acoustics, Speech, and Signal Processing},
     address = {Rhodes, Greece},
     year = {2023},
     month = jun,
     keywords = {Speech recognition ; Self-supervised learning},
     hal_id = {hal-04076307},
     hal_version = {v1}
   }

2022

1. LATENT AND ADVERSARIAL DATA AUGMENTATION FOR SOUND EVENT DETECTION AND CLASSIFICATION

   D. Perera , S. Essid, and G. Richard

   In International workshop on Detection and Classiffication of Acoustic Scenes and Events (DCASE) , Nov 2022

   Abs Bib PDF

   Invariance-based learning is a promising approach in deep learning. Among other benefits, it can mitigate the lack of diversity of available datasets and increase the interpretability of trained models. To this end, practitioners often use a consistency cost penalizing the sensitivity of a model to a set of carefully selected data augmentations. However, there is no consensus about how these augmentations should be selected. In this paper, we study the behavior of several augmentation strategies. We consider the task of sound event detection and classification for our experiments. In particular, we show that transformations operating on the internal layers of a deep neural network are beneficial for this task.

   @inproceedings{perera:hal-03782827,
     title = {{Latent and Adversarial Data Augmentation for Sound Event Detection and Classification}},
     author = {Perera, David and Essid, Slim and Richard, Ga{\"e}l},
     url = {https://hal.science/hal-03782827},
     booktitle = {{International workshop on Detection and Classiffication of Acoustic Scenes and Events (DCASE)}},
     address = {Nancy, France},
     year = {2022},
     month = nov,
     keywords = {sound event detection ; data augmentation ; adversarial learning},
     hal_id = {hal-03782827},
     hal_version = {v1}
   }

2. IMPACT DE PERTURBATIONS INTERNES SUR L ENTRAINEMENT DE RESEAUX PROFONDS POUR LA DETECTION D EVENEMENTS SONORES

   D. Perera , S. Essid, and G. Richard

   In Colloque Francophone de Traitement du Signal et des Images (GRETSI) , Sep 2022

   Bib PDF

   @inproceedings{perera:hal-03759651,
     title = {Impact de perturbations internes sur l entrainement de reseaux profonds pour la detection d evenements sonores},
     author = {Perera, David and Essid, Slim and Richard, Gael},
     url = {https://hal.telecom-paris.fr/hal-03759651},
     booktitle = {Colloque Francophone de Traitement du Signal et des Images (GRETSI)},
     address = {Nancy, France},
     year = {2022},
     month = sep,
     hal_id = {hal-03759651},
     hal_version = {v1}
   }

3. 

   PRETEXT TASKS SELECTION FOR MULTITASK SELF-SUPERVISED AUDIO REPRESENTATION LEARNING

   S. Zaiem , T. Parcollet , S. Essid, and A. Heba

   IEEE Journal of Selected Topics in Signal Processing, Sep 2022

   Abs Bib PDF

   Through solving pretext tasks, self-supervised learning leverages unlabeled data to extract useful latent representations replacing traditional input features in the downstream task. In audio/speech signal processing, a wide range of features where engineered through decades of research efforts. As it turns out, learning to predict such features (a.k.a pseudo-labels) has proven to be a particularly relevant pretext task, leading to useful self-supervised representations which prove to be effective for downstream tasks. However, methods and common practices for combining such pretext tasks for better performance on the downstream task have not been explored and understood properly. In fact, the process relies almost exclusively on a computationally heavy experimental procedure, which becomes intractable with the increase of the number of pretext tasks. This paper introduces a method to select a group of pretext tasks among a set of candidates. The method we propose estimates calibrated weights for the partial losses corresponding to the considered pretext tasks during the self-supervised training process. The experiments conducted on automatic speech recognition, speaker and emotion recognition validate our approach, as the groups selected and weighted with our method perform better than classic baselines, thus facilitating the selection and combination of relevant pseudo-labels for self-supervised representation learning.

   @article{9846981,
     author = {Zaiem, Salah and Parcollet, Titouan and Essid, Slim and Heba, Abdelwahab},
     journal = {IEEE Journal of Selected Topics in Signal Processing},
     title = {Pretext Tasks Selection for Multitask Self-Supervised Audio Representation Learning},
     year = {2022},
     volume = {16},
     number = {6},
     pages = {1439-1453},
     doi = {10.1109/JSTSP.2022.3195430},
   }

4. AUTOMATIC DATA AUGMENTATION SELECTION AND PARAMETRIZATION IN CONTRASTIVE SELF-SUPERVISED SPEECH REPRESENTATION LEARNING

   S. Zaiem , T. Parcollet , and S. Essid

   In Proc. Interspeech 2022 , Sep 2022

   Bib PDF

   @inproceedings{zaiem22_interspeech,
     author = {Zaiem, Salah and Parcollet, Titouan and Essid, Slim},
     title = {Automatic Data Augmentation Selection and Parametrization in Contrastive Self-Supervised Speech Representation Learning},
     year = {2022},
     booktitle = {Proc. Interspeech 2022},
     pages = {669--673},
     doi = {10.21437/Interspeech.2022-10191},
   }

5. LEARNING MULTI-LEVEL REPRESENTATIONS FOR HIERARCHICAL MUSIC STRUCTURE ANALYSIS

   M. Buisson , B. McFee , S. Essid, and H. Crayencour

   In International Society for Music Information Retrieval (ISMIR) , Dec 2022

   Abs Bib PDF

   Recent work in music structure analysis has shown the potential of deep features to highlight the underlying structure of music audio signals. Despite promising results achieved by such representations, dealing with the inherent hierarchical aspect of music structure remains a challenging problem. Because different levels of segmentation can be considered as equally valid, specifically designed representations should be optimized to improve hierarchical structure analysis. In this work, unsupervised learning of such representations using a contrastive approach operating at different timescales is explored. The proposed system is evaluated on flat and multi-level music segmentation. By leveraging both time and the hierarchical organization of music structure, we show that the obtained deep embeddings can encode meaningful patterns and improve segmentation at various levels of granularity.

   @inproceedings{buisson:hal-03780032,
     address = {Bengaluru, India},
     author = {Buisson, Morgan and McFee, Brian and Essid, Slim and Crayencour, Hélène C.},
     booktitle = {International Society for Music Information Retrieval (ISMIR)},
     hal_id = {hal-03780032},
     hal_version = {v1},
     month = dec,
     title = {Learning Multi-Level Representations for Hierarchical Music Structure Analysis},
     url = {https://hal.archives-ouvertes.fr/hal-03780032},
     year = {2022},
   }

6. OPINIONS IN INTERACTIONS : NEW ANNOTATIONS OF THE SEMAINE DATABASE

   V. Barrière , C. Clavel , and S. Essid

   In LREC , Jun 2022

   Bib PDF

   @inproceedings{barriere:hal-04276012,
     title = {{Opinions in Interactions : New Annotations of the SEMAINE Database}},
     author = {Barri{\`e}re, Valentin and Clavel, Chlo{\'e} and Essid, Slim},
     url = {https://hal.science/hal-04276012},
     booktitle = {{LREC}},
     address = {Marseille, France},
     year = {2022},
     month = jun,
     keywords = {Opinion Multimodal Machine Learning Interactions ; Opinion ; Multimodal Machine Learning ; Interactions},
     hal_id = {hal-04276012},
     hal_version = {v1}
   }

2021

1. 

   DNN-BASED MASK ESTIMATION FOR DISTRIBUTED SPEECH ENHANCEMENT IN SPATIALLY UNCONSTRAINED MICROPHONE ARRAYS

   N. Furnon , R. Serizel , S. Essid, and I. Illina

   IEEE/ACM Transactions on Audio, Speech and Language Processing, Jun 2021

   Abs Bib PDF

   Deep neural network (DNN)-based speech enhancement algorithms in microphone arrays have now proven to be efficient solutions to speech understanding and speech recognition in noisy environments. However, in the context of ad-hoc microphone arrays, many challenges remain and raise the need for distributed processing. In this paper, we propose to extend a previously introduced distributed DNN-based time-frequency mask estimation scheme that can efficiently use spatial information in form of so-called compressed signals which are pre-filtered target estimations. We study the performance of this algorithm named Tango under realistic acoustic conditions and investigate practical aspects of its optimal application. We show that the nodes in the microphone array cooperate by taking profit of their spatial coverage in the room. We also propose to use the compressed signals not only to convey the target estimation but also the noise estimation in order to exploit the acoustic diversity recorded throughout the microphone array.

   @article{furnon:hal-02985867,
     title = {DNN-based mask estimation for distributed speech enhancement in spatially unconstrained microphone arrays},
     author = {Furnon, Nicolas and Serizel, Romain and Essid, Slim and Illina, Irina},
     url = {https://hal.archives-ouvertes.fr/hal-02985867},
     journal = {IEEE/ACM Transactions on Audio, Speech and Language Processing},
     volume = {29},
     pages = {2310 - 2323},
     year = {2021},
     doi = {10.1109/TASLP.2021.3092838},
     hal_id = {hal-02985867},
     hal_version = {v3},
   }

2. USER-GUIDED ONE-SHOT DEEP MODEL ADAPTATION FOR MUSIC SOURCE SEPARATION

   g. Cantisani , A. Ozerov , S. Essid, and G. Richard

   In 2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) , Oct 2021

   Bib PDF

   @inproceedings{cantisani:hal-03219350,
     title = {User-guided one-shot deep model adaptation for music source separation},
     author = {Cantisani, giorgia and Ozerov, Alexey and Essid, Slim and Richard, Gael},
     url = {https://hal.telecom-paris.fr/hal-03219350},
     booktitle = {2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
     year = {2021},
     month = oct,
     address = {New Paltz, USA},
     keywords = {Music Source Separation ; User-guided Source Separation ; One-shot Domain Adaptation},
     hal_id = {hal-03219350},
     hal_version = {v3}
   }

3. ATTENTION-BASED DISTRIBUTED SPEECH ENHANCEMENT FOR UNCONSTRAINED MICROPHONE ARRAYS WITH VARYING NUMBER OF NODES

   N. Furnon , R. Serizel , S. Essid, and I. Illina

   In European Signal Processing Conference (EUSIPCO) , Aug 2021

   Bib PDF

   @inproceedings{furnon:hal-03259801,
     title = {Attention-based distributed speech enhancement for unconstrained microphone arrays with varying number of nodes},
     author = {Furnon, Nicolas and Serizel, Romain and Essid, Slim and Illina, Irina},
     url = {https://hal.archives-ouvertes.fr/hal-03259801},
     booktitle = {European Signal Processing Conference (EUSIPCO)},
     address = {Dublin/ Virtual, Ireland},
     organization = {IEEE},
     year = {2021},
     month = aug,
     keywords = {Speech enhancement ; distributed processing ; attention mechanisms ; ad-hoc microphone arrays},
     hal_id = {hal-03259801},
     hal_version = {v1}
   }

4. DISTRIBUTED SPEECH SEPARATION IN SPATIALLY UNCONSTRAINED MICROPHONE ARRAYS

   N. Furnon , R. Serizel , I. Illina , and S. Essid

   In ICASSP 2021 - 46th International Conference on Acoustics, Speech, and Signal Processing , Jun 2021

   Bib PDF

   @inproceedings{furnon:hal-02985794,
     title = {Distributed speech separation in spatially unconstrained microphone arrays},
     author = {Furnon, Nicolas and Serizel, Romain and Illina, Irina and Essid, Slim},
     url = {https://hal.archives-ouvertes.fr/hal-02985794},
     booktitle = {ICASSP 2021 - 46th International Conference on Acoustics, Speech, and Signal Processing},
     address = {Toronto, Canada},
     year = {2021},
     month = jun,
     keywords = {Speech separation ; Microphone arrays ; Distributed processing ; Speech separation},
     hal_id = {hal-02985794},
     hal_version = {v2}
   }

5. NEURO-STEERED MUSIC SOURCE SEPARATION WITH EEG-BASED AUDITORY ATTENTION DECODING AND CONTRASTIVE-NMF

   G. Cantisani , S. Essid, and G. Richard

   In ICASSP 2021 - 46th International Conference on Acoustics, Speech, and Signal Processing , Jun 2021

   Abs Bib PDF

   We propose a novel informed music source separation paradigm, which can be referred to as neuro-steered music source separation. More precisely, the source separation process is guided by the user’s selective auditory attention decoded from his/her EEG response to the stimulus. This high-level prior information is used to select the desired instrument to isolate and to adapt the generic source separation model to the observed signal. To this aim, we leverage the fact that the attended instrument’s neural encoding is substantially stronger than the one of the unattended sources left in the mixture. This "contrast" is extracted using an attention decoder and used to inform a source separation model based on non-negative matrix fac-torization named Contrastive-NMF. The results are promising and show that the EEG information can automatically select the desired source to enhance and improve the separation quality.

   @inproceedings{cantisani:hal-02978978,
     title = {NEURO-STEERED MUSIC SOURCE SEPARATION WITH EEG-BASED AUDITORY ATTENTION DECODING AND CONTRASTIVE-NMF},
     author = {Cantisani, Giorgia and Essid, Slim and Richard, Gael},
     url = {https://hal.telecom-paris.fr/hal-02978978},
     booktitle = {ICASSP 2021 - 46th International Conference on Acoustics, Speech, and Signal Processing},
     address = {Toronto, Canada},
     year = {2021},
     month = jun,
     keywords = {Index Terms-Audio source separation ; Auditory attention decoding ; Polyphonic music ; EEG ; Audio source separation},
     hal_id = {hal-02978978},
     hal_version = {v4}
   }

6. CONDITIONAL INDEPENDENCE FOR PRETEXT TASK SELECTION IN SELF-SUPERVISED SPEECH REPRESENTATION LEARNING

   S. Zaiem , T. Parcollet , and S. Essid

   In Interspeech , Aug 2021

   Bib PDF

   @inproceedings{zaiem21_interspeech,
     author = {Zaiem, Salah and Parcollet, Titouan and Essid, Slim},
     title = {Conditional Independence for Pretext Task Selection in Self-Supervised Speech Representation Learning},
     year = {2021},
     month = aug,
     booktitle = {Interspeech},
     pages = {2851--2855},
     doi = {10.21437/Interspeech.2021-1027},
   }

2020

1. METHOD AND SYSTEM FOR BROADCASTING A MULTICHANNEL AUDIO STREAM TO TERMINALS OF SPECTATORS ATTENDING A SPORTS EVENT

   R. Blouet , and S. Essid

   Patent Application, Sep 2020

   Bib PDF

   @article{SE:patent20,
     author = {Blouet, Raphael and Essid, Slim},
     title = {Method and System for Broadcasting a Multichannel Audio Stream to Terminals of Spectators Attending a Sports Event},
     year = {2020},
     month = sep,
     journal = {Patent Application},
     number = {US 2021/0014627 A1}
   }

2. DNN-BASED DISTRIBUTED MULTICHANNEL MASK ESTIMATION FOR SPEECH ENHANCEMENT IN MICROPHONE ARRAYS

   N. Furnon , R. Serizel , I. Illina , and S. Essid

   In ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing , May 2020

   Bib PDF

   @inproceedings{furnon:hal-02389159,
     title = {DNN-Based Distributed Multichannel Mask Estimation for Speech Enhancement in Microphone Arrays},
     author = {Furnon, Nicolas and Serizel, Romain and Illina, Irina and Essid, Slim},
     url = {https://hal.archives-ouvertes.fr/hal-02389159},
     booktitle = {ICASSP 2020 - 45th International Conference on Acoustics, Speech, and Signal Processing},
     address = {Barcelona, Spain},
     year = {2020},
     month = may,
     keywords = {Index Terms-Speech enhancement ; dis- tributed processing ; microphone arrays ; Distributed processing ; Speech enhancement},
     hal_id = {hal-02389159},
     hal_version = {v3}
   }

2019

1. 

   WEAKLY SUPERVISED REPRESENTATION LEARNING FOR AUDIO-VISUAL SCENE ANALYSIS

   S. Parekh , S. Essid, A. Ozerov , N. Duong , P. Pérez , and G. Richard

   IEEE/ACM Transactions on Audio, Speech, and Language Processing, May 2019

   Abs Bib PDF

   Audiovisual (AV) representation learning is an important task from the perspective of designing machines with the ability to understand complex events. To this end, we propose a novel multimodal framework that instantiates multiple instance learning. Specifically, we develop methods that identify events and localize corresponding AV cues in unconstrained videos. Importantly, this is done using weak labels where only video-level event labels are known without any information about their location in time. We show that the learnt representations are useful for performing several tasks such as event/object classification, audio event detection, audio source separation and visual object localization. An important feature of our method is its capacity to learn from unsynchronized audiovisual events. We also demonstrate our framework’s ability to separate out the audio source of interest through a novel use of nonnegative matrix factorization. State-of-the-art classification results, with a F1-score of 65.0, are achieved on DCASE 2017 smart cars challenge data with promising generalization to diverse object types such as musical instruments. Visualizations of localized visual regions and audio segments substantiate our system’s efficacy, especially when dealing with noisy situations where modality-specific cues appear asynchronously.

   @article{8926380,
     author = {Parekh, S. and Essid, Slim and Ozerov, A. and Duong, N. Q. K. and Pérez, P. and Richard, G.},
     journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
     title = {Weakly Supervised Representation Learning for Audio-Visual Scene Analysis},
     year = {2019},
     volume = {28},
     number = {},
     pages = {416-428},
   }

2. ON-THE-FLY DETECTION OF USER ENGAGEMENT DECREASE IN SPONTANEOUS HUMAN-ROBOT INTERACTION

   A. Ben Youssef , G. Varni , S. Essid, and C. Clavel

   International Journal of Social Robotics, Jan 2019

   Bib

   @article{benyoussef:hal-02288044,
     title = {On-the-fly Detection of User Engagement Decrease in Spontaneous Human-Robot Interaction},
     author = {Ben Youssef, Atef and Varni, Giovanna and Essid, Slim and Clavel, Chloe},
     url = {https://hal.telecom-paris.fr/hal-02288044},
     journal = {International Journal of Social Robotics},
     hal_local_reference = {ABY:IJSR-2019},
     year = {2019},
     month = jan,
     keywords = {User engagement decrease ; Socially assistive robot ; HRI in public space ; Real-time detection},
     hal_id = {hal-02288044},
     hal_version = {v1}
   }

3. A MULTIMODAL MOVIE REVIEW CORPUS FOR FINE-GRAINED OPINION MINING

   A. Garcia , S. Essid, F. DAlche-Buc , and C. Clavel

   Jan 2019

   Bib

   @techreport{DBLP:journals/corr/abs-1902-10102,
     author = {Garcia, Alexandre and Essid, Slim and DAlche-Buc, Florence and Clavel, Chloe},
     title = {A multimodal movie review corpus for fine-grained opinion mining},
     volume = {abs/1902.10102},
     year = {2019},
     url = {http://arxiv.org/abs/1902.10102},
     archiveprefix = {arXiv},
     eprint = {1902.10102},
     timestamp = {Fri, 24 May 2019 10:20:38 +0200},
     biburl = {https://dblp.org/rec/bib/journals/corr/abs-1902-10102},
     bibsource = {dblp computer science bibliography, https://dblp.org}
   }

4. FROM THE TOKEN TO THE REVIEW: A HIERARCHICAL MULTIMODAL APPROACH TO OPINION MINING

   A. Garcia , P. Colombo , F. DAlche-Buc , S. Essid, and C. Clavel

   In 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing , Nov 2019

   Bib

   @inproceedings{Garcia2019,
     author = {Garcia, Alexandre and Colombo, Pierre and DAlche-Buc, Florence and Essid, Slim and Clavel, Chloe},
     booktitle = {2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing},
     title = {From the Token to the Review: A Hierarchical Multimodal approach to Opinion Mining},
     year = {2019},
     month = nov,
     address = {Hong Kong, China}
   }

5. MAD-EEG: AN EEG DATASET FOR DECODING AUDITORY ATTENTION TO A TARGET INSTRUMENT IN POLYPHONIC MUSIC

   G. Cantisani , G. Tregoat , S. Essid, and G. Richard

   In Speech, Music and Mind (SMM19), Satellite workshop of Interspeech 2019 , Nov 2019

   Bib

   @inproceedings{Cantisani2019b,
     author = {Cantisani, Giorgia and Tregoat, Gabriel and Essid, Slim and Richard, Gael},
     booktitle = {Speech, Music and Mind (SMM19), Satellite workshop of Interspeech 2019},
     title = {MAD-EEG: an EEG dataset for decoding auditory attention to a target	instrument in polyphonic music},
     year = {2019},
     address = {Vienna, Austria}
   }

6. IDENTIFY, LOCATE AND SEPARATE: AUDIO-VISUAL OBJECT EXTRACTION IN LARGE VIDEO COLLECTIONS USING WEAK SUPERVISION

   S. Parekh , A. Ozerov , S. Essid, N. Duong , P. Perez , and G. Richard

   In 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) , Oct 2019

   Bib

   @inproceedings{Parekh2019,
     author = {Parekh, Sanjeel and Ozerov, Alexey and Essid, Slim and Duong, Ngoc Q. K. and Perez, Patrick and Richard, Gael},
     booktitle = {2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
     title = {Identify, Locate and Separate: Audio-visual Object Extraction in Large Video Collections Using Weak Supervision},
     year = {2019},
     month = oct,
     address = {New Paltz, USA}
   }

7. EEG-BASED DECODING OF AUDITORY ATTENTION TO A TARGET INSTRUMENT IN POLYPHONIC MUSIC

   G. Cantisani , S. Essid, and G. Richard

   In 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) , Oct 2019

   Bib

   @inproceedings{Cantisani2019,
     author = {Cantisani, Giorgia and Essid, Slim and Richard, Gael},
     booktitle = {2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
     title = {EEG-based Decoding of Auditory Attention to a Target Instrument in Polyphonic Music},
     year = {2019},
     address = {New Paltz, USA},
     month = oct
   }

8. SAMBASET: A DATASET OF HISTORICAL SAMBA DE ENREDO RECORDINGS FOR COMPUTATIONAL MUSIC ANALYSIS

   L. Maia , M. Fuentes , L. Biscainho , M. Rocamora , and S. Essid

   In The 20th International Society for Music Information Retrieval Conference , Nov 2019

   Bib

   @inproceedings{Maia2019,
     author = {Maia, Lucas S. and Fuentes, Magdalena and Biscainho, Luiz W. P. and Rocamora, Martin and Essid, Slim},
     booktitle = {The 20th International Society for Music Information Retrieval Conference},
     title = {SAMBASET: A Dataset of Historical Samba de Enredo Recordings for Computational Music Analysis},
     month = nov,
     address = {Delft, The Netherlands},
     year = {2019}
   }

9. TRACKING BEATS AND MICROTIMING IN AFRO-LATIN AMERICAN MUSIC USING CONDITIONAL RANDOM FIELDS AND DEEP LEARNING

   M. Fuentes , L. Maia , M. Rocamora , L. Biscainho , H. Crayencour , S. Essid, and J. Bello

   In The 20th International Society for Music Information Retrieval Conference , Nov 2019

   Bib

   @inproceedings{Fuentes2019b,
     author = {Fuentes, Magdalena and Maia, Lucas S. and Rocamora, Martin and Biscainho, Luiz W. P. and Crayencour, Helene C. and Essid, Slim and Bello, Juan Pablo},
     booktitle = {The 20th International Society for Music Information Retrieval Conference},
     title = {Tracking Beats And Microtiming In Afro-latin American
     	music Using Conditional Random Fields and Deep Learning},
     month = nov,
     address = {Delft, The Netherlands},
     year = {2019}
   }

10. A MUSIC STRUCTURE INFORMED DOWNBEAT TRACKING SYSTEM USING SKIP-CHAIN CONDITIONAL RANDOM FIELDS AND DEEP LEARNING

    M. Fuentes , B. McFee , H. Crayencour , S. Essid, and J. Bello

    In IEEE International Conference on Acoustics, Speech and Signal processing , May 2019

    Bib

    @inproceedings{Fuentes2019,
      author = {Fuentes, Magdalena and McFee, Brian and Crayencour, Helene and Essid, Slim and Bello, Juan Pablo},
      booktitle = {IEEE International Conference on Acoustics, Speech and Signal processing},
      title = {A Music Structure Informed Downbeat Tracking System Using Skip-Chain Conditional Random Fields and Deep Learning},
      month = may,
      address = {Brighton, UK},
      year = {2019}
    }

11. AUDIOVISUAL ANALYSIS OF MUSIC PERFORMANCES: OVERVIEW OF AN EMERGING FIELD

    Z. Duan , S. Essid , C. Liem , G. Richard , and G. Sharma

    IEEE Signal Processing Magazine, Jan 2019

    Bib

    @article{Duan2019,
      author = {Duan, Z. and Essid, S. and Liem, C. C. S. and Richard, G. and Sharma, G.},
      journal = {IEEE Signal Processing Magazine},
      title = {Audiovisual Analysis of Music Performances: Overview of an Emerging Field},
      year = {2019},
      volume = {36},
      number = {1},
      pages = {63-73},
      keywords = {audio signal processing;music;audiovisual analysis;audio modality;music recordings;audio-only media;automated music analysis;audio signals;acoustic music rendering;Music;Visualization;Task analysis;Instruments;Multiple signal classification;Signal processing;Cameras;Acoustics},
      doi = {10.1109/MSP.2018.2875511},
      issn = {1053-5888},
      month = jan
    }

12. EARLY DETECTION OF USER ENGAGEMENT BREAKDOWN IN SPONTANEOUS HUMAN-HUMANOID INTERACTION

    A. Ben Youssef , C. Clavel , and S. Essid

    IEEE Transactions on Affective Computing, Jan 2019

    Bib

    @article{BenYoussef2019,
      author = {Ben Youssef, A and Clavel, C and Essid, S},
      doi = {10.1109/TAFFC.2019.2898399},
      issn = {1949-3045},
      journal = {IEEE Transactions on Affective Computing},
      keywords = {Robots;Electric breakdown;Feature extraction;Predi},
      pages = {1},
      title = {Early Detection of User Engagement Breakdown in Spontaneous Human-Humanoid Interaction},
      year = {2019}
    }

2018

1. PROCEDE ET SYSTEME DE DIFFUSION D UN FLUX AUDIO MULTICANAL A DES TERMINAUX DE SPECTATEURS ASSISTANT A UN EVENEMENT SPORTIF

   R. Blouet , and S. Essid

   Patent Application, Mar 2018

   Bib PDF

   @article{SE:patent18,
     author = {Blouet, Raphael and Essid, Slim},
     title = {Procede et Systeme de Diffusion d un Flux Audio Multicanal a des terminaux de spectateurs assistant a un evenement sportif},
     year = {2018},
     month = mar,
     journal = {Patent Application},
     number = {1852774}
   }

2. MEDLEY-SOLOS-DB: A CROSS-COLLECTION DATASET FOR MUSICAL INSTRUMENT RECOGNITION

   V. Lostanlen , C. Cella , R. Bittner , and S. Essid

   Sep 2018

   Bib

   @misc{lostanlen_vincent_2018_1344103,
     author = {Lostanlen, Vincent and Cella, Carmine-Emanuele and Bittner, Rachel and Essid, Slim},
     title = {Medley-solos-DB: a cross-collection dataset for
     	musical instrument recognition},
     month = sep,
     year = {2018},
     doi = {10.5281/zenodo.1344103},
     url = {https://doi.org/10.5281/zenodo.1344103}
   }

3. EEG-BASED INTER-SUBJECT CORRELATION SCHEMES IN A STIMULI-SHARED FRAMEWORK: INTERPLAY WITH VALENCE AND AROUSAL

   A. Hajlaoui , M. Chetouani , and S. Essid

   Sep 2018

   Bib

   @article{Hajlaoui2018,
     archiveprefix = {arXiv},
     arxivid = {1809.08273},
     author = {Hajlaoui, Ayoub and Chetouani, Mohamed and Essid, Slim},
     eprint = {1809.08273},
     month = sep,
     title = {EEG-based Inter-Subject Correlation Schemes in a Stimuli-Shared Framework: Interplay with Valence and Arousal},
     url = {https://arxiv.org/abs/1809.08273},
     year = {2018}
   }

4. ANALYSIS OF COMMON DESIGN CHOICES IN DEEP LEARNING SYSTEMS FOR DOWNBEAT TRACKING

   M. Fuentes , B. McFee , H. Crayencour , S. Essid, and J. Bello

   In Proceedings of the 19th International Society for Music Information Retrieval Conference , Sep 2018

   Bib

   @inproceedings{Fuentes2018,
     address = {Paris, France},
     author = {Fuentes, Magdalena and McFee, Brian and Crayencour, Helene C and Essid, Slim and Bello, Juan Pablo},
     booktitle = {Proceedings of the 19th International Society for Music Information Retrieval Conference},
     doi = {10.5281/zenodo.1492355},
     month = sep,
     pages = {106--112},
     publisher = {ISMIR},
     title = {Analysis of Common Design Choices in Deep Learning Systems for Downbeat Tracking},
     url = {https://doi.org/10.5281/zenodo.1492355},
     year = {2018}
   }

5. MAIN MELODY ESTIMATION WITH SOURCE-FILTER NMF AND CRNN

   D. Basaran , S. Essid, and G. Peeters

   In Proceedings of the 19th International Society for Music Information Retrieval Conference , Sep 2018

   Bib

   @inproceedings{Basaran2018,
     address = {Paris, France},
     author = {Basaran, Dogac and Essid, Slim and Peeters, Geoffroy},
     booktitle = {Proceedings of the 19th International Society for Music Information Retrieval Conference},
     doi = {10.5281/zenodo.1492349},
     month = sep,
     pages = {82--89},
     publisher = {ISMIR},
     title = {Main Melody Estimation with Source-Filter NMF and CRNN},
     url = {https://doi.org/10.5281/zenodo.1492349},
     year = {2018}
   }

6. A ROBUST AUDIO CLASSIFICATION SYSTEM FOR DETECTING PULMONARY EDEMA

   K. Hong , S. Essid , W. Ser , and D. Foo

   Biomedical Signal Processing and Control, Sep 2018

   Abs Bib

   In this paper we present a robust audio classification system to efficiently detect pulmonary edema. The system uses a feature learning technique based on (NMF), then classified with logistic regression. A study was done to compare feature engineering approaches with feature selection techniques against NMF. Different NMF schemes were investigated and also compared with Principal Component Analysis. NMF scored 95% F1 score, which was superior to feature engineering techniques that had scores from 83% to 93%. Background noise collected from hospitals and speech from a speech corpus database was used to simulate noisy data. The system was then tested using noisy data. The best NMF scheme scored 74%, while other feature engineering techniques scored lower; from 66% to 71%. NMF was also used as a signal enhancement tool. It improved the F1 score to 77%. Lastly, only inhalations from breath sounds were considered and this further improved classification results to 86%. The proposed robust classification system using NMF thus proved to be an effective method for audio-based detection of pulmonary edema. If implemented in real-time, the proposed system can be used as a screening tool.

   @article{Hong2018,
     author = {Hong, K. J. and Essid, S. and Ser, W. and Foo, D. C.G.},
     doi = {10.1016/j.bspc.2018.07.004},
     issn = {17468108},
     journal = {Biomedical Signal Processing and Control},
     keywords = {Biomedical signal processing,Feature learning,Non-negative matrix factorization,Pulmonary edema,Robust testing},
     title = {A robust audio classification system for detecting pulmonary edema},
     year = {2018}
   }

7. MULTI-TASK FEATURE LEARNING FOR EEG-BASED EMOTION RECOGNITION USING GROUP NONNEGATIVE MATRIX FACTORIZATION

   A. Hajlaoui , M. Chetouani , and S. Essid

   In The European Signal Processing Conference (EUSIPCO) , Sep 2018

   Bib

   @inproceedings{Hajlaoui2019,
     address = {Rome, Italy},
     author = {Hajlaoui, Ayoub and Chetouani, Mohamed and Essid, Slim},
     booktitle = {The European Signal Processing Conference (EUSIPCO)},
     title = {Multi-task Feature Learning for EEG-based Emotion Recognition Using Group Nonnegative Matrix Factorization},
     month = sep,
     year = {2018}
   }

8. STRUCTURED OUTPUT LEARNING WITH ABSTENTION: APPLICATION TO ACCURATE OPINION PREDICTION

   A. Garcia , S. Essid, C. Clavel , and F. DAlche-Buc

   In International Conference on Machine Learning (ICML) , Jul 2018

   Abs Bib

   Motivated by Supervised Opinion Analysis, we propose a novel framework devoted to Structured Output Learning with Abstention (SOLA). The structure prediction model is able to abstain from predicting some labels in the structured output at a cost chosen by the user in a flexible way. For that purpose, we decompose the problem into the learning of a pair of predictors, one devoted to structured abstention and the other, to structured output prediction. To compare fully labeled training data with predictions potentially containing abstentions, we define a wide class of asymmetric abstention-aware losses. Learning is achieved by surrogate regression in an appropriate feature space while prediction with abstention is performed by solving a new pre-image problem. Thus, SOLA extends recent ideas about Structured Output Prediction via surrogate problems and calibration theory and enjoys statistical guarantees on the resulting excess risk. Instantiated on a hierarchical abstention-aware loss, SOLA is shown to be relevant for fine-grained opinion mining and gives state-of-the-art results on this task. Moreover, the abstention-aware representations can be used to competitively predict user-review ratings based on a sentence-level opinion predictor.

   @inproceedings{Garcia2018b,
     author = {Garcia, Alexandre and Essid, Slim and Clavel, Chloe and DAlche-Buc, Florence},
     month = jul,
     title = {Structured Output Learning with Abstention: Application to Accurate Opinion Prediction},
     booktitle = {International Conference on Machine Learning (ICML)},
     year = {2018},
     address = {Stockholm, Sweeden}
   }

9. WEAKLY SUPERVISED REPRESENTATION LEARNING FOR UNSYNCHRONIZED AUDIO-VISUAL EVENTS

   S. Parekh , S. Essid, A. Ozerov , Q. Duong , P. Perez , and G. Richard

   In CVPR Workshop on Sight and Sound (WSS) , Jun 2018

   Bib

   @inproceedings{Parekh2018b,
     author = {Parekh, Sanjeel and Essid, Slim and Ozerov, Alexey and Duong, Quang-Khanh-Ngoc and Perez, Patrick and Richard, Gael},
     booktitle = {CVPR Workshop on Sight and Sound (WSS)},
     month = jun,
     title = {Weakly Supervised Representation Learning for Unsynchronized Audio-Visual Events},
     year = {2018},
     address = {Salt Lake City, USA},
     annote = {
       { @inproceedings{Parekh2018b,
         author = {Parekh, Sanjeel and Essid, Slim and Ozerov, Alexey and Duong, Quang-Khanh-Ngoc and Perez, Patrick and Richard, Gael},
         booktitle = {CVPR Workshop on Sight and Sound (WSS)},
         month = jun,
         title = {Weakly Supervised Representation Learning for Unsynchronized Audio-Visual Events},
         year = 2018,
         address = {Salt Lake City, USA}
       }}
     }
   }

10. WEAKLY SUPERVISED REPRESENTATION LEARNING FOR UNSYNCHRONIZED AUDIO-VISUAL EVENTS

    S. Parekh , S. Essid, A. Ozerov , Q. Duong , P. Perez , and G. Richard

    Apr 2018

    Abs Bib

    Audio-visual representation learning is an important task from the perspective of designing machines with the ability to understand complex events. To this end, we propose a novel multimodal framework that instantiates multiple instance learning. We show that the learnt representations are useful for classifying events and localizing their characteristic audio-visual elements. The system is trained using only video-level event labels without any timing information. An important feature of our method is its capacity to learn from unsynchronized audio-visual events. We achieve state-of-the-art results on a large-scale dataset of weakly-labeled audio event videos. Visualizations of localized visual regions and audio segments substantiate our system s efficacy, especially when dealing with noisy situations where modality-specific cues appear asynchronously.

    @techreport{Parekh2018,
      archiveprefix = {arXiv},
      arxivid = {1804.07345},
      number = {arXiv:1804.07345},
      author = {Parekh, Sanjeel and Essid, Slim and Ozerov, Alexey and Duong, Quang-Khanh-Ngoc and Perez, Patrick and Richard, Gael},
      eprint = {1804.07345},
      month = apr,
      title = {Weakly Supervised Representation Learning for Unsynchronized Audio-Visual Events},
      url = {http://arxiv.org/abs/1804.07345},
      year = {2018}
    }

11. STRUCTURED OUTPUT LEARNING WITH ABSTENTION: APPLICATION TO ACCURATE OPINION PREDICTION

    A. Garcia , S. Essid, C. Clavel , and F. DAlche-Buc

    Mar 2018

    Abs Bib

    Motivated by Supervised Opinion Analysis, we propose a novel framework devoted to Structured Output Learning with Abstention (SOLA). The structure prediction model is able to abstain from predicting some labels in the structured output at a cost chosen by the user in a flexible way. For that purpose, we decompose the problem into the learning of a pair of predictors, one devoted to structured abstention and the other, to structured output prediction. To compare fully labeled training data with predictions potentially containing abstentions, we define a wide class of asymmetric abstention-aware losses. Learning is achieved by surrogate regression in an appropriate feature space while prediction with abstention is performed by solving a new pre-image problem. Thus, SOLA extends recent ideas about Structured Output Prediction via surrogate problems and calibration theory and enjoys statistical guarantees on the resulting excess risk. Instantiated on a hierarchical abstention-aware loss, SOLA is shown to be relevant for fine-grained opinion mining and gives state-of-the-art results on this task. Moreover, the abstention-aware representations can be used to competitively predict user-review ratings based on a sentence-level opinion predictor.

    @techreport{Garcia2018,
      archiveprefix = {arXiv},
      arxivid = {1803.08355},
      number = {arXiv:1803.08355},
      author = {Garcia, Alexandre and Essid, Slim and Clavel, Chloe and DAlche-Buc, Florence},
      eprint = {1803.08355},
      month = mar,
      title = {Structured Output Learning with Abstention: Application to Accurate Opinion Prediction},
      url = {http://arxiv.org/abs/1803.08355},
      year = {2018}
    }

12. AN ENSEMBLE LEARNING APPROACH TO DETECT EPILEPTIC SEIZURES FROM LONG INTRACRANIAL EEG RECORDINGS

    J. Schiratti , J. Le Douget , M. Le Van Quyen , S. Essid, and A. Gramfort

    In International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , Apr 2018

    Bib PDF

    @inproceedings{schiratti:hal-01724272,
      title = {An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings},
      author = {Schiratti, Jean-Baptiste and Le Douget, Jean-Eudes and Le Van Quyen, Michel and Essid, Slim and Gramfort, Alexandre},
      url = {https://hal.archives-ouvertes.fr/hal-01724272},
      booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      address = {Calgary, Canada},
      year = {2018},
      month = apr,
      keywords = {Intracranial EEG ; Epilepsy and seizures ; Machine learning},
    }

13. ATTITUDE CLASSIFICATION IN ADJACENCY PAIRS OF A HUMAN-AGENT INTERACTION WITH HIDDEN CONDITIONAL RANDOM FIELDS

    V. Barriere , C. Clavel , and S. Essid

    In International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , Apr 2018

    Bib

    @inproceedings{BARRIERE-18,
      author = {Barriere, Valentin and Clavel, Chloe and Essid, Slim},
      title = {Attitude Classification in Adjacency Pairs of a Human-Agent Interaction with Hidden Conditional Random Fields},
      booktitle = {International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
      address = {Calgary, Canada},
      year = {2018},
      month = apr,
      annote = {category=inproceedings language=en audience=2 state=toappear dept=ids group=s2a id=17744}
    }

14. METHOD FOR AUDIO-VISUAL EVENTS CLASSIFICATION AND LOCALIZATION, AND CORRESPONDING APPARATUS, COMPUTER READABLE PROGRAM, PRODUCT AND COMPUTER READABLE STORAGE MEDIUM

    S. Parekh , S. Essid, A. Ozerov , Q. Duong , P. Perez , and G. Richard

    Patent Application, Apr 2018

    Bib

    @article{SP:brevet18,
      author = {Parekh, Sanjeel and Essid, Slim and Ozerov, Alexey and Duong, Quang-Khanh-Ngoc and Perez, Patrick and Richard, Gael},
      title = {Method for audio-visual events classification and localization, and corresponding apparatus, computer readable program, product and computer readable storage medium},
      year = {2018},
      month = apr,
      journal = {Patent Application},
      number = {180049},
      annote = {category=brevet language=en state=published dept=ids group=s2a id=18015}
    }

2017

1. MATRIX CO-FACTORISATION AND APPLICATIONS TO MUSIC ANALYSIS

   S. Essid

   In Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML) 2017 , Aug 2017

   Bib PDF

   @inproceedings{SE:ICML17,
     author = {Essid, Slim},
     title = {Matrix Co-Factorisation and Applications to Music Analysis},
     booktitle = {Machine Learning for Music Discovery Workshop, International Conference on Machine Learning (ICML) 2017},
     address = {Sydney, Australia},
     year = {2017},
     month = aug,
     annote = {category=invite language=en audience=1 state=published dept=ids group=s2a id=18054},
   }

2. NONNEGATIVE FEATURE LEARNING METHODS FOR ACOUSTIC SCENE CLASSIFICATION

   V. Bisot , R. Serizel , S. Essid, and G. Richard

   In DCASE 2017 - Workshop on Detection and Classification of Acoustic Scenes and Events , Nov 2017

   Bib PDF

   @inproceedings{bisot:hal-01636627,
     author = {Bisot, Victor and Serizel, Romain and Essid, Slim and Richard, Gael},
     title = {Nonnegative Feature Learning Methods for Acoustic Scene Classification},
     booktitle = {DCASE 2017 - Workshop on Detection and Classification of Acoustic Scenes and Events},
     address = {Munich, Germany},
     year = {2017},
     month = nov,
     keywords = {Feature learning;Nonnegative Matrix Factorization;Deep Neural Networks},
     annote = {category=inproceedings language=en audience=2 state=published dept=ids group=s2a documentURL=http://biblio.telecom-paristech.fr/cgi-bin/download.cgi?id=17481 id=17481},
   }

3. METHOD FOR PROCESSING AN INPUT AUDIO SIGNAL AND CORRESPONDING ELECTRONIC DEVICE

   S. Parekh , S. Essid, A. Ozerov , Q. Duong , P. Perez , and G. Richard

   Patent Application, Nov 2017

   Bib

   @article{SP:brevet17a,
     author = {Parekh, Sanjeel and Essid, Slim and Ozerov, Alexey and Duong, Quang-Khanh-Ngoc and Perez, Patrick and Richard, Gael},
     title = {Method for Processing an input audio signal and corresponding electronic device},
     year = {2017},
     journal = {Patent Application},
     number = {170054},
     annote = {category=article language=en state=published dept=ids group=s2a id=18014}
   }

4. COMPUTATIONAL ANALYSIS OF SOUND SCENES AND EVENTS

   R. Serizel , V. Bisot , S. Essid, and G. Richard

   Nov 2017

   Bib

   @inbook{Serizel2017,
     author = {Serizel, Romain and Bisot, Victor and Essid, Slim and Richard, Gael},
     chapter = {Acoustic Features for Environmental Sound Analysis},
     title = {Computational Analysis of Sound Scenes and Events},
     year = {2017},
     editor = {Virtanen, Tuomas and Ellis, Dan and Plumbley, Mark},
     publisher = {Springer International Publishing AG}
   }

5. COMPUTATIONAL ANALYSIS OF SOUND SCENES AND EVENTS

   S. Essid, S. Parekh , Q. Duong , A. Ozerov , and R. Serizel

   Nov 2017

   Bib

   @inbook{Essid2017,
     author = {Essid, Slim and Parekh, Sanjeel and Duong, Quang-Khanh-Ngoc and Ozerov, Alexey and Serizel, Romain},
     chapter = {Multiview Approaches to Event Detection and Scene Analysis},
     title = {Computational Analysis of Sound Scenes and Events},
     year = {2017},
     editor = {Virtanen, Tuomas and Ellis, Dan and Plumbley, Mark},
     publisher = {Springer International Publishing AG}
   }

6. UE-HRI: A NEW DATASET FOR THE STUDY OF USER ENGAGEMENT IN SPONTANEOUS HUMAN-ROBOT INTERACTIONS

   A. Ben Youssef , C. Clavel , S. Essid, M. Bilac , M. Chamoux , and A. Lim

   In ACM International Conference on Multimodal Interaction , Nov 2017

   Bib PDF

   @inproceedings{Benyoussef2017,
     author = {Ben Youssef, Atef and Clavel, Chloe and Essid, Slim and Bilac, Miriam and Chamoux, Marine and Lim, Angelica},
     booktitle = {ACM International Conference on Multimodal Interaction},
     title = {UE-HRI: A New Dataset for the Study of User Engagement in Spontaneous Human-Robot Interactions},
     year = {2017},
     month = nov,
     address = {Glasgow, Scotland},
   }

7. LEVERAGING DEEP NEURAL NETWORKS WITH NONNEGATIVE REPRESENTATIONS FOR IMPROVED ENVIRONMENTAL SOUND CLASSIFICATION

   V. Bisot , R. Serizel , S. Essid, and G. Richard

   In IEEE International Workshop on Machine Learning for Signal Processing (MLSP) , Sep 2017

   Bib PDF

   @inproceedings{Bisot2017c,
     author = {Bisot, Victor and Serizel, Romain and Essid, Slim and Richard, Gael},
     booktitle = {IEEE International Workshop on	Machine Learning for Signal Processing (MLSP)},
     title = {Leveraging Deep Neural Networks with Nonnegative Representations for Improved Environmental Sound Classification},
     year = {2017},
     month = sep,
     address = {Tokyo, Japan},
   }

8. GUIDING AUDIO SOURCE SEPARATION BY VIDEO OBJECT INFORMATION

   S. Parekh , S. Essid, A. Ozerov , Q. Duong , P. Perez , and G. Richard

   In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) , Oct 2017

   Bib PDF

   @inproceedings{Parekh2017b,
     address = {New Paltz, New York, U.S.A},
     author = {Parekh, Sanjeel and Essid, Slim and Ozerov, Alexey and Duong, Quang-Khanh-Ngoc and Perez, Patrick and Richard, Gael},
     booktitle = {IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
     month = oct,
     title = {Guiding Audio Source Separation by Video Object Information},
     year = {2017}
   }

9. EMOEEG: A NEW MULTIMODAL DATASET FOR DYNAMIC EEG-BASED EMOTION RECOGNITION WITH AUDIOVISUAL ELICITATION

   A. Conneau , A. Hajlaoui , M. Chetouani , and S. Essid

   In The European Signal Processing Conference (EUSIPCO) , Oct 2017

   Bib PDF

   @inproceedings{Conneau2017,
     address = {Kos island, Greece},
     author = {Conneau, Anne-Claire and Hajlaoui, Ayoub and Chetouani, Mohamed and Essid, Slim},
     booktitle = {The European Signal Processing Conference (EUSIPCO)},
     title = {EMOEEG: a New Multimodal Dataset for Dynamic EEG-based Emotion Recognition with Audiovisual Elicitation},
     year = {2017}
   }

10. OPINION DYNAMICS MODELING FOR MOVIE REVIEW TRANSCRIPTS

    V. Barriere , C. Clavel , and S. Essid

    In Interspeech , Oct 2017

    Bib PDF

    @inproceedings{Barriere2017,
      address = {Stockholm, Sweeden},
      author = {Barriere, Valentin and Clavel, Chloe and Essid, Slim},
      booktitle = {Interspeech},
      title = {Opinion Dynamics Modeling for Movie Review Transcripts},
      year = {2017},
    }

11. OVERLAPPING SOUND EVENT DETECTION WITH SUPERVISED NONNEGATIVE MATRIX FACTORIZATION

    V. Bisot , S. Essid, and G. Richard

    In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Oct 2017

    Bib PDF

    @inproceedings{Bisot2017b,
      address = {New Orleans, USA},
      author = {Bisot, Victor and Essid, Slim and Richard, Gael},
      booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      title = {Overlapping Sound Event Detection with Supervised Nonnegative Matrix Factorization},
      year = {2017},
    }

12. FEATURE LEARNING WITH MATRIX FACTORIZATION APPLIED TO ACOUSTIC SCENE CLASSIFICATION

    V. Bisot , R. Serizel , S. Essid, and G. Richard

    IEEE Transactions on Audio, Speech, and Language Processing (TASLP), Oct 2017

    Bib PDF

    @article{Bisot2017,
      author = {Bisot, Victor and Serizel, Romain and Essid, Slim and Richard, Gael},
      journal = {IEEE Transactions on Audio, Speech, and Language Processing (TASLP)},
      title = {Feature Learning with Matrix Factorization Applied to Acoustic Scene Classification},
      year = {2017},
    }

13. SUPERVISED GROUP NONNEGATIVE MATRIX FACTORISATION WITH SIMILARITY CONSTRAINTS AND APPLICATIONS TO SPEAKER IDENTIFICATION

    R. Serizel , V. Bisot , S. Essid, and G. Richard

    In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Oct 2017

    Bib PDF

    @inproceedings{Serizel2018,
      address = {New Orleans, USA},
      author = {Serizel, Romain and Bisot, Victor and Essid, Slim and Richard, Gael},
      booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
      title = {Supervised Group Nonnegative Matrix Factorisation with Similarity Constraints and Applications to Speaker Identification},
      year = {2017},
    }

14. MOTION INFORMED AUDIO SOURCE SEPARATION

    S. Parekh , S. Essid, A. Ozerov , Q. Duong , P. Perez , and G. Richard

    In IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP) , Oct 2017

    Bib PDF

    @inproceedings{Parekh2017,
      address = {New Orleans, USA},
      author = {Parekh, Sanjeel and Essid, Slim and Ozerov, Alexey and Duong, Quang-Khanh-Ngoc and Perez, Patrick and Richard, Gael},
      booktitle = {IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP)},
      title = {Motion Informed Audio Source Separation},
      year = {2017},
    }

2016

1. DISPOSITIF A CASQUE AUDIO PERFECTIONNE

   S. Essid, and R. Blouet

   Patent Application, Nov 2016

   Bib PDF

   @article{SE:patent16,
     author = {Essid, Slim and Blouet, Raphael},
     title = {Dispositif a Casque Audio Perfectionne},
     year = {2016},
     month = nov,
     journal = {Patent Application},
     number = {1661324}
   }

2. SUPERVISED NONNEGATIVE MATRIX FACTORIZATION FOR ACOUSTIC SCENE CLASSIFICATION

   V. Bisot , R. Serizel , S. Essid, and G. Richard

   In IEEE international evaluation campaign on detection and classification of acousitc scenes and events (DCASE 2016) , Sep 2016

   Bib PDF

   @inproceedings{Bisot2016b,
     author = {Bisot, Victor and Serizel, Romain and Essid, Slim and Richard, Gael},
     keywords = {Acoustic Scene Classification ; Feature learning ;},
     booktitle = {IEEE international evaluation campaign on detection and classification of acousitc scenes and events (DCASE 2016)},
     month = sep,
     title = {Supervised nonnegative matrix factorization for acoustic scene classification},
     year = {2016},
   }

3. DOWNBEAT DETECTION WITH CONDITIONAL RANDOM FIELDS AND DEEP LEARNED FEATURES

   S. Durand , and S. Essid

   In The 17th International Society for Music Information Retrieval Conference (ISMIR) , Aug 2016

   Bib PDF

   @inproceedings{SD:ISMIR-16,
     title = {Downbeat Detection with Conditional Random Fields and Deep Learned Features},
     author = {Durand, Simon and Essid, Slim},
     booktitle = {The 17th International Society for Music Information Retrieval Conference (ISMIR)},
     month = aug,
     year = {2016},
     address = {New York City, USA},
   }

4. MINI-BATCH STOCHASTIC APPROACHES FOR ACCELERATED MULTIPLICATIVE UPDATES IN NONNEGATIVE MATRIX FACTORISATION WITH BETA-DIVERGENCE

   R. Serizel , S. Essid, and G. Richard

   In IEEE International Workshop on Machine Learning for Signal Processing (MLSP) , Sep 2016

   Bib PDF

   @inproceedings{RS:MLSP-2016,
     author = {Serizel, Romain and Essid, Slim and Richard, Gael},
     title = {Mini-batch stochastic approaches for accelerated multiplicative updates in nonnegative matrix factorisation with beta-divergence},
     booktitle = {IEEE International Workshop on Machine Learning for Signal Processing (MLSP)},
     year = {2016},
     month = sep,
     keywords = {Nonnegative matrix factorisation, GPGPU, multiplicative rules, online learning},
     annote = {category=inproceedings language=en audience=2 state=submitted dept=tsi group=aao      documentURL=http://biblio.telecom-paristech.fr/cgi-bin/download.cgi?id=16155 id=16155},
   }

5. MACHINE LISTENING TECHNIQUES AS A COMPLEMENT TO VIDEO IMAGE ANALYSIS IN FORENSICS

   R. Serizel , V. Bisot , S. Essid, and G. Richard

   In The International Conference on Image Processing (ICIP) , Oct 2016

   Bib PDF

   @inproceedings{RS:ICIP-2016,
     author = {Serizel, Romain and Bisot, Victor and Essid, Slim and Richard, Gael},
     title = {Machine listening techniques as a complement to video image analysis in forensics},
     booktitle = {The International Conference on Image Processing (ICIP)},
     year = {2016},
     month = oct,
     annote = {category=inproceedings language=en audience=2 state=toappear dept=tsi group=aao documentURL=http://biblio.telecom-paristech.fr/cgi-bin/download.cgi?id=16154 id=16154},
   }

6. ACOUSTIC SCENE CLASSIFICATION WITH MATRIX FACTORIZATION FOR UNSUPERVISED FEATURE LEARNING

   V. Bisot , R. Serizel , S. Essid, and G. Richard

   In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Mar 2016

   Bib PDF

   @inproceedings{Bisot2016,
     author = {Bisot, Victor and Serizel, Romain and Essid, Slim and Richard, Gael},
     title = {Acoustic scene classification with matrix factorization for unsupervised feature learning},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
     address = {Shanghai, China},
     year = {2016},
     month = mar,
     keywords = {Acoustic scene classification, unsupervised feature learning,   matrix factorization},
     annote = {category=inproceedings language=en audience=2 state=toappear dept=tsi group=aao id=15948},
   }

7. GROUP NONNEGATIVE MATRIX FACTORISATION WITH SPEAKER AND SESSION VARIABILITY COMPENSATION FOR SPEAKER IDENTIFICATION

   R. Serizel , S. Essid, and G. Richard

   In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Mar 2016

   Bib PDF

   @inproceedings{Serizel2016a,
     author = {Serizel, Romain and Essid, Slim and Richard, Gael},
     title = {Group nonnegative matrix factorisation with speaker and session variability compensation for speaker identification},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
     address = {Shanghai, China},
     year = {2016},
     month = mar,
     keywords = {Nonnegative matrix factorisation, spectrogram factorisation, feature learning, speaker variability, speaker identification},
     annote = {category=inproceedings language=en audience=2 state=toappear dept=tsi group=aao documentURL=http://biblio.telecom-paristech.fr/cgi-bin/download.cgi?id=15957 id=15957},
   }

2015

1. CONTRIBUTIONS IN MACHINE LEARNING FOR MULTIMODAL DATA ANALYSIS: METHODS, ALGORITHMS AND SYSTEMS FOR TEMPORALLY STRUCTURED DATA

   S. Essid

   Université Pierre et Marie Curie , Sep 2015

   Habilitation Thesis

   Bib

   @phdthesis{SE:hdr,
     author = {Essid, Slim},
     title = {Contributions in Machine Learning for Multimodal Data Analysis: Methods, Algorithms and Systems for Temporally Structured Data},
     school = {Universit\é Pierre et Marie Curie},
     year = {2015},
     note = {Habilitation Thesis},
     month = sep
   }

2. TPT-DANCE&ACTIONS : UN CORPUS MULTIMODAL D’ACTIVITES HUMAINES

   A. Masurelle , A. Sekkat , S. Essid, and G. Richard

   Revue Traitement du Signal, Sep 2015

   Bib PDF

   @article{Masurelle2015,
     title = {TPT-Dance&Actions : un corpus multimodal d’activites humaines},
     number = {4},
     journal = {Revue Traitement du Signal},
     author = {Masurelle, Aymeric and Sekkat, A. Rida and Essid, Slim and Richard, Gael},
     year = {2015},
   }

3. MELODY EXTRACTION BY CONTOUR CLASSIFICATION

   R. Bittner , J. Salmon , S. Essid, and J. Bello

   In International Conference on Music Information Retrieval (ISMIR) , Sep 2015

   Bib PDF

   @inproceedings{Bittner2015,
     author = {Bittner, Rachel and Salmon, Justin and Essid, Slim and Bello, J. P.},
     booktitle = {International Conference on Music Information Retrieval (ISMIR)},
     title = {Melody Extraction by Contour Classification},
     address = {Malaga, Spain},
     year = {2015},
   }

4. HOG AND SUBBAND POWER DISTRIBUTION IMAGE FEATURES FOR ACOUSTIC SCENE CLASSIFICATION

   V. Bisot , S. Essid, and G. Richard

   In European Signal Processing Conference (EUSIPCO) , Sep 2015

   Bib PDF

   @inproceedings{Bisot2015,
     author = {Bisot, Victor and Essid, Slim and Richard, Gael},
     booktitle = {European Signal Processing Conference (EUSIPCO)},
     title = {HOG and subband power distribution image features for acoustic scene classification},
     address = {Nice, France},
     year = {2015},
   }

5. A CONDITIONAL RANDOM FIELD SYSTEM FOR BEAT TRACKING

   T. Fillon , C. Joder , S. Durand , and S. Essid

   In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Apr 2015

   Bib PDF

   @inproceedings{TF:ICASSP-15,
     author = {Fillon, Thomas and Joder, Cyril and Durand, Simon and Essid, Slim},
     title = {A Conditional Random Field System for Beat Tracking},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
     address = {Brisbane, Australia},
     year = {2015},
     month = apr,
     annote = {category=inproceedings language=en audience=2 state=toappear dept=tsi group=aao id=15366}
   }

2014

1. SOFT NONNEGATIVE MATRIX CO-FACTORIZATION

   N. Seichepine , S. Essid, C. Fevotte , and O. Cappe

   IEEE Transactions on Signal Processing, Apr 2014

   Bib PDF

   @article{Seichepine_Essid_Fevotte_Cappe_2014,
     title = {Soft nonnegative matrix co-factorization},
     volume = {PP},
     url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6908018},
     doi = {10.1109/TSP.2014.2360141},
     abstractnote = {This work introduces a new framework for nonnegative matrix factorization (NMF) in multisensor or multimodal data configurations, where taking into account the mutual dependence that exists between the related parallel streams of data is expected to improve performance. In contrast with previous works that focused on co-factorization methods  where some factors are shared by the different modalities we propose a soft co-factorization scheme which accounts for possible local discrepancies across modalities or channels. This objective is formalized as an optimization problem where concurrent factorizations are jointly performed while being tied by a coupling term that penalizes differences between the related factor matrices associated with different modalities. We provide majorization-minimization (MM) algorithms for three common measures of fit, the squared Euclidean norm, the Kullback-Leibler divergence and the Itakura-Saito divergence, and two possible coupling variants, using either the l1 or the squared Euclidean norm of differences. The approach is shown to achieve promising performance in two audio-related tasks: multimodal speaker diarization using audiovisual data and audio source separation using stereo data.},
     number = {99},
     journal = {IEEE Transactions on Signal Processing},
     author = {Seichepine, Nicolas and Essid, Slim and Fevotte, Cedric and Cappe, Olivier},
     year = {2014},
   }

2. PIECEWISE CONSTANT NONNEGATIVE MATRIX FACTORIZATION

   N. Seichepine , S. Essid, C. Fevotte , and O. Cappe

   In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , May 2014

   Bib PDF

   @inproceedings{NS:ICASSP-14,
     author = {Seichepine, Nicolas and Essid, Slim and Fevotte, Cedric and Cappe, Olivier},
     title = {Piecewise constant Nonnegative matrix factorization},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
     address = {Florence, Italy},
     year = {2014},
     month = may,
   }

3. ASSESSMENT OF NEW SPECTRAL FEATURES FOR EEG-BASED EMOTION RECOGNITION

   A. Conneau , and S. Essid

   In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , May 2014

   Bib PDF

   @inproceedings{AC:ICASSP-14,
     author = {Conneau, Anne-Claire and Essid, Slim},
     title = {Assessment of new spectral features for EEG-based emotion recognition},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
     address = {Florence, Italy},
     year = {2014},
     month = may,
   }

4. GESTURE RECOGNITION USING A NMF-BASED REPRESENTATION OF MOTION-TRACES EXTRACTED FROM DEPTH SILHOUETTES

   A. Masurelle , S. Essid, and G. Richard

   In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , May 2014

   Bib PDF

   @inproceedings{AM:ICASSP-14,
     author = {Masurelle, Aymeric and Essid, Slim and Richard, Gael},
     title = {Gesture recognition using a NMF-based representation of motion-traces extracted from depth silhouettes},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
     address = {Florence, Italy},
     year = {2014},
     month = may,
   }

2013

1. CO-FACTORISATION DOUCE EN MATRICES NON-NEGATIVES. APPLICATION AU REGROUPEMENT MULTIMODAL DE LOCUTEURS

   N. Seichepine , S. Essid, C. Fevotte , and O. Cappe

   In GRETSI , Sep 2013

   Bib

   @inproceedings{gretsi-13,
     author = {Seichepine, Nicolas and Essid, Slim and Fevotte, Cedric and Cappe, Olivier},
     title = {Co-factorisation douce en matrices non-negatives. Application au regroupement multimodal de locuteurs},
     booktitle = {GRETSI},
     address = {Brest, France},
     year = {2013},
     month = sep,
     keywords = {NMF, multimodal},
     annote = {category=inproceedings language=fr audience=1 state=toappear dept=tsi group=aao,sta id=14269}
   }

2. NONNEGATIVE TENSOR FACTORIZATION FOR SINGLE-CHANNEL EEG ARTIFACT REJECTION

   C. Damon , A. Liutkus , A. Gramfort , and S. Essid

   In IEEE International Workshop on Machine Learning for Signal Processing , Sep 2013

   Bib PDF

   @inproceedings{CD:MLSP-13,
     author = {Damon, Cecilia and Liutkus, Antoine and Gramfort, Alexandre and Essid, Slim},
     title = {Nonnegative Tensor Factorization for Single-Channel EEG Artifact Rejection},
     booktitle = {IEEE International Workshop on Machine Learning for Signal Processing},
     address = {Southampton, UK},
     year = {2013},
     month = sep,
     keywords = {EEG, NTF, NMF},
   }

3. EXPLORING NEW FEATURES FOR MUSIC CLASSIFICATION

   R. Foucard , S. Essid, G. Richard , and M. Lagrange

   In International Workshop on Image and Audio Analysis for Multimedia Interactive Services (WIAMIS) , Jul 2013

   Bib PDF

   @inproceedings{RF:Wiamis2013,
     author = {Foucard, Remi and Essid, Slim and Richard, Gael and Lagrange, Mathieu},
     title = {Exploring new features for music classification},
     booktitle = {International Workshop on Image and Audio Analysis for Multimedia Interactive Services (WIAMIS)},
     address = {Paris, France},
     year = {2013},
     month = jul,
   }

4. MULTIMODAL CLASSIFICATION OF DANCE MOVEMENTS USING BODY JOINT TRAJECTORIES AND STEP SOUNDS

   A. Masurelle , S. Essid, and G. Richard

   In International Workshop on Image and Audio Analysis for Multimedia Interactive Services (WIAMIS) , Jul 2013

   Bib PDF

   @inproceedings{AM:WIAMIS-13,
     author = {Masurelle, Aymeric and Essid, Slim and Richard, Gael},
     title = {Multimodal classification of dance movements using body joint trajectories and step sounds},
     booktitle = {International Workshop on Image and Audio Analysis for Multimedia Interactive Services (WIAMIS)},
     address = {Paris, France},
     year = {2013},
     month = jul,
   }

5. PROBABILISTIC DANCE PERFORMANCE ALIGNMENT BY FUSION OF MULTIMODAL FEATURES

   A. Dremeau , and S. Essid

   In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , May 2013

   Bib PDF

   @inproceedings{Dremeau2013a,
     author = {Dremeau, Angelique and Essid, Slim},
     title = {Probabilistic dance performance alignment by fusion of multimodal features },
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
     address = {Vancouver, Canada},
     year = {2013},
     month = may,
     annote = {category=inproceedings language=en audience=2 state=toappear dept=tsi group=aao id=13252},
   }

6. SOFT NONNEGATIVE MATRIX CO-FACTORIZATION WITH APPLICATION TO MULTIMODAL SPEAKER DIARIZATION

   N. Seichepine , S. Essid, C. Fevotte , and O. Cappe

   In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , May 2013

   Bib PDF

   @inproceedings{ICASSP-13-SEICHEPINE,
     author = {Seichepine, Nicolas and Essid, Slim and Fevotte, Cedric and Cappe, Olivier},
     title = {Soft Nonnegative Matrix Co-factorization with Application to Multimodal Speaker Diarization},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)},
     address = {Vancouver},
     year = {2013},
     month = may,
     keywords = {Nonnegative matrix factorization, cofactorization, multimodality, speaker diarization},
     annote = {category=inproceedings language=en audience=2 state=toappear dept=tsi group=aao,sta id=13313},
   }

7. NONNEGATIVE MATRIX FACTORIZATION FOR SINGLE-CHANNEL EEG ARTIFACT REJECTION

   C. Damon , A. Liutkus , A. Gramfort , and S. Essid

   In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) , May 2013

   Bib PDF

   @inproceedings{CD:ICASSP-13,
     author = {Damon, Cecilia and Liutkus, Antoine and Gramfort, Alexandre and Essid, Slim},
     title = {Nonnegative Matrix Factorization for Single-Channel EEG Artifact Rejection},
     booktitle = {IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) },
     address = {Vancouver, Canada},
     year = {2013},
     month = may,
     annote = {category=inproceedings language=en audience=2 state=toappear dept=tsi group=aao id=13264},
   }

8. A MULTIMODAL APPROACH TO SPEAKER DIARIZATION ON TV TALK-SHOWS

   F. Vallet , S. Essid, and J. Carrive

   IEEE Transactions on Multimedia, May 2013

   Abs Bib PDF

   In this article, we propose solutions to the problem of speaker diarization of TV talk-shows, a problem for which adapted multimodal approaches, relying on other streams of data than only audio, remain largely under exploited. Hence we propose an original system that leverages prior knowledge on the structure of this type of content, especially the visual information relating to the active speakers, for an improved diarization performance. The architecture of this system can be decomposed into two main stages. First a reliable training set is created, in an unsupervised fashion, for each participant of the TV program being processed. This data is assembled by the association of visual and audio descriptors carefully selected in a clustering cascade. Then, Support Vector Machines are used for the classification of the speech data (of a given TV program). The performance of this new architecture is assessed on two French talk-show collections: Le Grand Échiquier and On na pas tout dit. The results show that our new system outperforms state-of-the-art methods, thus evidencing the effectiveness of kernel-based methods, as well as visual cues, in multimodal approaches to speaker diarization of challenging contents such as TV talk-shows.

   @article{FV:TM-12,
     author = {Vallet, F. and Essid, Slim and Carrive, J.},
     journal = {IEEE Transactions on Multimedia},
     title = {A Multimodal Approach to Speaker Diarization on TV Talk-Shows},
     year = {2013},
     volume = {15},
     number = {3},
     pages = {509-520},
     keywords = {speaker recognition;support vector machines;French talk show collection;TV program;TV talk shows;audio descriptors;clustering cascade;diarization performance;kernel based method;multimodal approach;original system;reliable training set;speaker diarization;speech data;support vector machines;visual descriptors;Cameras;Databases;Microphones;NIST;Speech;TV;Visualization;Fusion;SVM classification;joint audiovisual processing;multimodality;speaker diarization;talk-show;unsupervised learning},
     doi = {10.1109/TMM.2012.2233724},
     issn = {1520-9210},
   }

9. LEARNING OPTIMAL FEATURES FOR POLYPHONIC AUDIO-TO-SCORE ALIGNMENT

   C. Joder , S. Essid, and G. Richard

   IEEE Transactions on Audio, Speech, and Language Processing, May 2013

   Bib PDF

   @article{CJ:TASLP-13,
     author = {Joder, Cyril and Essid, Slim and Richard, Gael},
     journal = {IEEE Transactions on Audio, Speech, and Language Processing},
     title = {Learning Optimal Features for Polyphonic Audio-to-Score Alignment},
     year = {2013},
     volume = {21},
     number = {10},
     pages = {2118-2128},
     keywords = {audio signal processing;maximum likelihood estimation;signal representation;CQT-based representation;audio observations;conditional random fields model;discriminative framework;feature functions design;heuristic mappings;learning optimal features;linear transformation;maximum likelihood criterion;musical recording;polyphonic audio-to-score alignment;polyphonic music;spectrogram;symbolic representation;symmetric Kull-back-Leibler divergence;template construction;template vectors;temporal constraints;Music information retrieval;audio-to-score alignment;conditional random fields;discriminative learning},
     doi = {10.1109/TASL.2013.2266794},
     issn = {1558-7916},
   }

10. SMOOTH NONNEGATIVE MATRIX FACTORIZATION FOR UNSUPERVISED AUDIOVISUAL DOCUMENT STRUCTURING

    S. Essid, and C. Fevotte

    IEEE Transactions on Multimedia, May 2013

    Bib PDF

    @article{SE:TMM-12,
      author = {Essid, Slim and Fevotte, Cedric},
      journal = {IEEE Transactions on Multimedia},
      title = {Smooth Nonnegative Matrix Factorization for Unsupervised Audiovisual Document Structuring},
      year = {2013},
      volume = {15},
      number = {2},
      pages = {415-425},
      keywords = {audio signal processing;document handling;hidden Markov models;matrix decomposition;minimisation;unsupervised learning;video databases;video signal processing;Kullback-Leibler divergence;NMF algorithm;audio modality;audio speaker diarization;cost function;hidden Markov model;histogram-of-count;latent structuring pattern;majorization-minimization technique;person-oriented video structuring task;political debate video database;smooth nonnegative matrix factorization;temporal smoothness constraint;unsupervised audiovisual document structuring;visual modality;Data models;Feature extraction;Histograms;Indexing;Telecommunications;Visualization;Vocabulary;Bag of features;content structuring;indexing;machine learning;matrix factorization;unsupervised classification;videos},
      doi = {10.1109/TMM.2012.2228474},
      issn = {1520-9210},
    }

2012

1. ANALYSIS OF DANCE MOVEMENTS USING GAUSSIAN PROCESSES

   A. Liutkus , A. Dremeau , D. Alexiadis , S. Essid, and P. Daras

   In ACM Multimedia , Nov 2012

   Bib

   @inproceedings{AL:ACM-12,
     author = {Liutkus, Antoine and Dremeau, Angelique and Alexiadis, D. and Essid, Slim and Daras, Petros},
     title = {Analysis of dance movements using Gaussian processes},
     booktitle = {ACM Multimedia},
     address = {Nara, Japan},
     year = {2012},
     month = nov,
     url = {http://hal.inria.fr/hal-00718791}
   }

2. DECOMPOSING THE VIDEO EDITING STRUCTURE OF A TALK-SHOW USING NONNEGATIVE MATRIX FACTORIZATION

   S. Essid, and C. Fevotte

   In International Conference on Image Processing (ICIP) , Oct 2012

   Bib

   @inproceedings{SE:ICIP-12,
     author = {Essid, Slim and Fevotte, Cedric},
     title = {Decomposing the Video Editing Structure of a Talk-show using Nonnegative Matrix Factorization},
     booktitle = {International Conference on Image Processing (ICIP)},
     address = {Orlando, FL, USA},
     year = {2012},
     month = oct,
     annote = {category=inproceedings language=en audience=2 state=toappear dept=tsi group=aao id=12528}
   }

3. MULTIMODAL MUSIC PROCESSING

   S. Essid, and G. Richard

   Oct 2012

   Bib PDF

   @inbook{SE:DFU-12,
     author = {Essid, Slim and Richard, Gael},
     chapter = {Fusion of Multimodal Information in Music Content Analysis},
     title = {Multimodal Music Processing},
     pages = {37--52},
     series = {Dagstuhl Follow-Ups},
     isbn = {978-3-939897-37-8},
     issn = {1868-8977},
     year = {2012},
     volume = {3},
     editor = {Muller, Meinard and Goto, Masataka and Schedl, Markus},
     publisher = {Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik},
     address = {Dagstuhl, Germany},
     url = {http://drops.dagstuhl.de/opus/volltexte/2012/3465},
     urn = {urn:nbn:de:0030-drops-34652},
     doi = {http://dx.doi.org/10.4230/DFU.Vol3.11041.37},
     annote = {Keywords: Multimodal music processing, music signals indexing and transcription, information fusion, audio, video},
   }

4. A MULTI-MODAL DANCE CORPUS FOR RESEARCH INTO INTERACTION BETWEEN HUMANS IN VIRTUAL ENVIRONMENTS

   S. Essid, X. Lin , M. Gowing , G. Kordelas , A. Aksay , P. Kelly , T. Fillon , Q. Zhang , A. Dielmann , V. Kitanovski , R. Tournemenne , A. Masurelle , E. Izquierdo , N. OConnor , P. Daras , and G. Richard

   Journal on Multimodal User Interfaces: Special issue on multimodal corpora, Oct 2012

   Bib PDF

   @article{SE:ICMI-12,
     author = {Essid, Slim and Lin, X. and Gowing, M. and Kordelas, G. and Aksay, A. and Kelly, P. and Fillon, Thomas and Zhang, Q. and Dielmann, A. and Kitanovski, V. and Tournemenne, R. and Masurelle, Aymeric and Izquierdo, E. and OConnor, N. E. and Daras, Petros and Richard, Gael},
     title = {A multi-modal dance corpus for research into interaction between humans in virtual environments},
     journal = {Journal on Multimodal User Interfaces: Special issue on multimodal corpora},
     year = {2012},
   }

5. AN ADVANCED VIRTUAL DANCE PERFORMANCE EVALUATOR

   S. Essid, D. Alexiadis , R. Tournemenne , M. Gowing , P. Kelly , D. Monhagan , P. Daras , A. Dremeau , and N. OConnor

   In IEEE International Conference on Acoustics, Speech and Signal Processing , Mar 2012

   Bib PDF

   @inproceedings{SE:ICASSP-12b,
     author = {Essid, Slim and Alexiadis, D. and Tournemenne, R. and Gowing, M. and Kelly, P. and Monhagan, D. and Daras, Petros and Dremeau, Angelique and OConnor, N. E.},
     title = {An Advanced Virtual Dance Performance Evaluator},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing},
     address = {Kyoto, Japan},
     year = {2012},
     month = mar,
     annote = {category=inproceedings language=en audience=2 state=toappear project=audiosig dept=tsi group=aao id=12271},
   }

6. A SINGLE-CLASS SVM BASED ALGORITHM FOR COMPUTING AN IDENTIFIABLE NMF

   S. Essid

   In IEEE International Conference on Acoustics, Speech and Signal Processing , Mar 2012

   Bib PDF

   @inproceedings{SE:ICASSP-12,
     author = {Essid, Slim},
     title = {A Single-class SVM Based Algorithm For Computing An Identifiable Nmf},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing},
     address = {Kyoto, Japan},
     year = {2012},
     month = mar,
     annote = {category=inproceedings language=en audience=2 state=toappear project=audiosig dept=tsi group=aao id=12270},
   }

7. A REGRESSIVE BOOSTING APPROACH TO AUTOMATIC AUDIO TAGGING BASED ON SOFT ANNOTATOR FUSION

   R. Foucard , S. Essid, M. Lagrange , and G. Richard

   In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Mar 2012

   Bib PDF

   @inproceedings{RF:ICASSP-12,
     author = {Foucard, Remi and Essid, Slim and Lagrange, Mathieu and Richard, Gael},
     title = {A Regressive Boosting Approach To Automatic Audio Tagging Based On Soft Annotator Fusion},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
     address = {Kyoto, Japan},
     year = {2012},
     month = mar,
     annote = {category=inproceedings language=en audience=2 state=toappear project=audiosig dept=tsi group=aao id=12269},
   }

2011

1. A MULTIMODAL DANCE CORPUS FOR RESEARCH INTO REAL-TIME INTERACTION BETWEEN HUMANS IN ONLINE VIRTUAL ENVIRONMENTS

   S. Essid, X. Lin , M. Gowing , G. Kordelas , A. Aksay , P. Kelly , T. Fillon , Q. Zhang , A. Dielmann , V. Kitanovski , R. Tournemenne , N. OConnor , P. Daras , and G. Richard

   In ICMI Workshop On Multimodal Corpora For Machine Learning , Nov 2011

   Bib PDF

   @inproceedings{SE:ICMI-11,
     author = {Essid, Slim and Lin, X. and Gowing, M. and Kordelas, G. and Aksay, A. and Kelly, P. and Fillon, Thomas and Zhang, Q. and Dielmann, A. and Kitanovski, V. and Tournemenne, R. and OConnor, N. E. and Daras, Petros and Richard, Gael},
     title = {A multimodal dance corpus for research into real-time interaction between humans in online virtual environments },
     booktitle = {ICMI Workshop On Multimodal Corpora For Machine Learning},
     address = {Alicante, Spain},
     year = {2011},
     month = nov,
     annote = {category=inproceedings language=en audience=2 state=published project=audiosig dept=tsi group=aao id=12272},
   }

2. AN AUDIO-DRIVEN VIRTUAL DANCE-TEACHING ASSISTANT

   S. Essid, Y. Grenier , M. Maazaoui , G. Richard , and R. Tournemenne

   In ACM Multimedia , Nov 2011

   Bib PDF

   @inproceedings{SE:ACM-MM-GC-2011,
     author = {Essid, Slim and Grenier, Yves and Maazaoui, Mounira and Richard, Gael and Tournemenne, R.},
     title = {An audio-driven virtual dance-teaching assistant},
     booktitle = {ACM Multimedia},
     address = {Scottsdale, Arizona, USA},
     year = {2011},
     month = nov,
     annote = {category=inproceedings language=fr audience=1 state=toappear project= dept=tsi group=aao id=11561}
   }

3. ENHANCED VISUALISATION OF DANCE PERFORMANCE FROM AUTOMATICALLY SYNCHRONISED MULTIMODAL RECORDINGS

   M. Gowing , P. Kelly , N. OConnor , E. Izquierdo , V. Kitanovski , X. Lin , Q. Zhang , C. Concolato , S. Essid, J. Feuvre , and R. Tournemenne

   In ACM Multimedia , Nov 2011

   Bib PDF

   @inproceedings{CC:ACM-MM-GC-2011,
     author = {Gowing, M. and Kelly, P. and OConnor, N. E. and Izquierdo, E. and Kitanovski, V. and Lin, X. and Zhang, Q. and Concolato, C. and Essid, Slim and Feuvre, J. Le and Tournemenne, R.},
     title = {Enhanced Visualisation of Dance Performance from Automatically Synchronised Multimodal Recordings},
     booktitle = {ACM Multimedia},
     address = {Scottsdale, Arizona, USA},
     year = {2011},
     month = nov,
     annote = {category=inproceedings language=en audience=2 state=toappear project=m2 dept=tsi group=aao,mm id=11552}
   }

4. AN INTERACTIVE SYSTEM FOR ELECTRO-ACOUSTIC MUSIC ANALYSIS

   S. Gulluni , S. Essid, O. Buisson , and G. Richard

   In International Conference on Music Information Retrieval (ISMIR) , Oct 2011

   Bib PDF

   @inproceedings{SG:ISMIR-11,
     author = {Gulluni, S. and Essid, Slim and Buisson, O. and Richard, Gael},
     title = {An interactive system for electro-acoustic music analysis},
     booktitle = {International Conference on Music Information Retrieval (ISMIR)},
     year = {2011},
     address = {Miami, U.S.A},
     month = oct,
   }

5. MULTI-SCALE TEMPORAL FUSION BY BOOSTING FOR MUSIC CLASSIFICATION

   R. Foucard , S. Essid, M. Lagrange , and G. Richard

   In International Conference on Music Information Retrieval (ISMIR) , Oct 2011

   Bib PDF

   @inproceedings{RF:ISMIR-11,
     author = {Foucard, Remi and Essid, Slim and Lagrange, Mathieu and Richard, Gael},
     title = {Multi-scale temporal fusion by boosting for music classification},
     booktitle = {International Conference on Music Information Retrieval (ISMIR)},
     year = {2011},
     address = {Miami, U.S.A},
     month = oct,
   }

6. OPTIMIZING THE MAPPING FROM A SYMBOLIC TO AN AUDIO REPRESENTATION FOR MUSIC-TO-SCORE ALIGNMENT

   C. Joder , S. Essid, and G. Richard

   In IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA) , Oct 2011

   Bib PDF

   @inproceedings{CJ:WASPAA-11,
     author = {Joder, Cyril and Essid, Slim and Richard, Gael},
     title = {Optimizing the Mapping from a Symbolic to an Audio Representation for Music-to-Score Alignment},
     booktitle = {IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)},
     year = {2011},
     address = {New Paltz, New York, U.S.A},
     month = oct,
   }

7. NONNEGATIVE MATRIX FACTORIZATION FOR UNSUPERVISED AUDIOVISUAL DOCUMENT STRUCTURING

   S. Essid, and C. Fevotte

   Oct 2011

   Bib PDF

   @techreport{SE:TREP-11,
     author = {Essid, Slim and Fevotte, Cedric},
     title = {Nonnegative matrix factorization for unsupervised audiovisual document structuring},
     year = {2011},
     institution = {HAL},
     number = {hal-00605886},
     url = {http://hal.archives-ouvertes.fr/hal-00605886/en/}
   }

8. SEMANTIQUE ET MULTIMODALITE EN ANALYSE DE L INFORMATION

   G. Adda , G. Chollet , S. Essid, T. Fillon , M. Garnier-Rizet , C. Hory , and L. Beltaifa-Zouari

   Oct 2011

   Bib

   @inbook{GA:IFMGBOOK,
     chapter = {Traitement des modalites "audio" et "parole"},
     title = {Semantique et multimodalite en analyse de l information},
     publisher = {Hermes/Lavoisier},
     year = {2011},
     editor = {Campedel, Marine and Hoogstel, Pierre},
     author = {Adda, G. and Chollet, G. and Essid, Slim and Fillon, Thomas and Garnier-Rizet, M. and Hory, C. and Beltaifa-Zouari, L.},
     owner = {essid},
     timestamp = {2011.02.28}
   }

9. TV CONTENT ANALYSIS: TECHNIQUES AND APPLICATIONS

   F. Vallet , S. Essid, J. Carrive , and G. Richard

   Oct 2011

   Bib

   @inbook{FV:TVBOOK-11,
     author = {Vallet, F. and Essid, Slim and Carrive, J. and Richard, G.},
     title = {TV Content Analysis: Techniques and Applications},
     editor = {Y. Kompatsiaris, B. Merialdo and Lian, S.},
     publisher = {CRC Press, Taylor Francis LLC},
     chapter = {High-level TV talk show structuring centered on speakers interventions},
     year = {2011}
   }

10. INTERACTIVE CLASSIFICATION OF SOUND OBJECTS FOR POLYPHONIC ELECTRO-ACOUSTIC MUSIC ANNOTATION

    S. Gulluni , S. Essid, O. Buisson , and G. Richard

    In AES 42nd International Conference , Jul 2011

    Bib PDF

    @inproceedings{SG:AES-11,
      author = {Gulluni, S. and Essid, Slim and Buisson, O. and Richard, Gael},
      title = {Interactive Classification of Sound Objects for Polyphonic Electro-Acoustic Music Annotation},
      booktitle = {AES 42nd International Conference},
      year = {2011},
      month = jul,
      address = {Ilmenau, Germany}
    }

11. MULTIMEDIA SEMANTICS: METADATA, ANALYSIS AND INTERACTION

    R. Benmokhtar , B. Huet , G. Richard , T. Declerck , and S. Essid

    Jul 2011

    Bib

    @inbook{RB:MMABOOK-11,
      chapter = {Feature Extraction for Multimedia Analysis},
      title = {Multimedia Semantics: Metadata, Analysis and Interaction},
      publisher = {Wiley},
      year = {2011},
      editor = {R. Troncy, B. Huet and Schenk, S.},
      author = {Benmokhtar, R. and Huet, B. and Richard, Gael and Declerck, T. and Essid, Slim},
      owner = {essid},
      timestamp = {2010.12.20}
    }

12. MULTIMEDIA SEMANTICS: METADATA, ANALYSIS AND INTERACTION

    S. Essid, M. Campedel , G. Richard , T. Piatrik , R. Benmokhtar , and B. Huet

    Jul 2011

    Bib

    @inbook{SE:MMABOOK-11,
      chapter = {Machine Learning Techniques for Multimedia Analysis},
      title = {Multimedia Semantics: Metadata, Analysis and Interaction},
      publisher = {Wiley},
      year = {2011},
      editor = {R. Troncy, B. Huet and Schenk, S.},
      author = {Essid, Slim and Campedel, M. and Richard, Gael and Piatrik, T. and Benmokhtar, R. and Huet, B.},
      owner = {essid},
      timestamp = {2010.12.20}
    }

13. A CONDITIONAL RANDOM FIELD FRAMEWORK FOR ROBUST AND SCALABLE AUDIO-TO-SCORE MATCHING

    C. Joder , S. Essid, and G. Richard

    IEEE Transactions on Audio, Speech and Language Processing, Nov 2011

    Bib PDF

    @article{CJ:TSALP-2011,
      author = {Joder, Cyril and Essid, Slim and Richard, Gael},
      title = {A Conditional Random Field Framework for Robust and Scalable Audio-to-Score
      	Matching},
      journal = {IEEE Transactions on Audio, Speech and Language Processing},
      volume = {19},
      pages = {2385 - 2397},
      number = {8},
      month = nov,
      year = {2011},
      owner = {essid},
      timestamp = {2011.02.10}
    }

14. HIDDEN DISCRETE TEMPO MODEL: A TEMPO-AWARE TIMING MODEL FOR AUDIO-TO-SCORE ALIGNMENT

    C. Joder , S. Essid, and G. Richard

    In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , May 2011

    Bib PDF

    @inproceedings{CJ:ICASSP-11,
      author = {Joder, Cyril and Essid, Slim and Richard, Gael},
      title = {Hidden Discrete Tempo Model: a Tempo-aware Timing Model for Audio-to-Score
      	Alignment},
      booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing
      	(ICASSP)},
      year = {2011},
      address = {Prague, Czech Republic},
      month = may,
      owner = {essid},
      timestamp = {2011.01.28}
    }

2010

1. A MULTIMODAL APPROACH TO INITIALISATION FOR TOP-DOWN SPEAKER DIARIZATION OF TELEVISION SHOWS

   S. Bozonnet , F. Vallet , N. Evans , S. Essid, J. Carrive , and G. Richard

   In European Signal Processing Conference (EUSIPCO) , Aug 2010

   Bib PDF

   @inproceedings{SB:Eusipco10,
     author = {Bozonnet, S. and Vallet, F. and Evans, N. and Essid, Slim and Carrive, J. and Richard, Gael},
     title = {A multimodal approach to initialisation for top-down speaker diarization
     	of television shows},
     booktitle = {European Signal Processing Conference (EUSIPCO)},
     year = {2010},
     address = {Allborg, Denmark},
     month = aug,
     annote = {category=inproceedings state=toappear project=audiosig dept=tsi group=aao
     	id=10394},
     file = {:D\:\\documents\\myPage\\papers\\SB_EUSIPCO-10.pdf:PDF},
     owner = {essid},
     timestamp = {2010.06.24}
   }

2. A CONDITIONAL RANDOM FIELD VIEWPOINT OF SYMBOLIC AUDIO-TO-SCORE MATCHING

   C. Joder , S. Essid, and G. Richard

   In ACM Multimedia 2010 , Oct 2010

   Bib

   @inproceedings{CJ:ACM-2010,
     author = {Joder, Cyril and Essid, Slim and Richard, Gael},
     title = {A Conditional Random Field Viewpoint of Symbolic Audio-to-Score Matching},
     booktitle = {ACM Multimedia 2010},
     year = {2010},
     address = {Florence, Italy},
     month = oct,
     owner = {essid},
     timestamp = {2010.07.06}
   }

3. APPROCHE HIÉRARCHIQUE POUR UN ALIGNEMENT MUSIQUE-SUR-PARTITION EFFICACE

   C. Joder , S. Essid, and G. Richard

   In Compression et Représentation des Signaux Audiovisuels (CORESA) , Oct 2010

   Received Young Researcher Award!

   Bib PDF

   @inproceedings{CJ:CORESA-2010,
     author = {Joder, Cyril and Essid, Slim and Richard, Gael},
     title = {Approche hi\érarchique pour un alignement musique-sur-partition
     	efficace},
     booktitle = {Compression et Repr\ésentation des Signaux Audiovisuels (CORESA)},
     year = {2010},
     address = {Lyon, France},
     month = oct,
     note = {Received Young Researcher Award!},
     annote = {category=inproceedings state=toappear project=audiosig dept=tsi group=aao
     	id=10418},
     file = {:D\:\\documents\\myPage\\papers\\CJ_CORESA-10.pdf:PDF},
     owner = {essid},
     timestamp = {2010.06.24}
   }

4. A COMPARATIVE STUDY OF TONAL ACOUSTIC FEATURES FOR A SYMBOLIC LEVEL MUSIC-TO-SCORE ALIGNMENT

   C. Joder , S. Essid, and G. Richard

   In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Mar 2010

   Bib PDF

   @inproceedings{CJ:ICASSP-2010,
     author = {Joder, Cyril and Essid, Slim and Richard, Gael},
     title = {A Comparative Study of tonal acoustic Features for a Symbolic Level
     	Music-to-Score Alignment},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing
     	(ICASSP)},
     year = {2010},
     address = {Dallas, TX, US},
     month = mar,
     annote = {category=inproceedings state=toappear project=audiosig dept=tsi group=aao
     	id=10174},
     file = {CJ_ICASSP-2010.pdf:./papers/CJ_ICASSP-10.pdf:PDF},
     owner = {essid},
     timestamp = {2010.03.10}
   }

5. AN IMPROVED HIERARCHICAL APPROACH FOR MUSIC-TO-SYMBOLIC SCORE ALIGNMENT

   C. Joder , S. Essid, and G. Richard

   In International Conference on Music Information Retrieval (ISMIR) , Aug 2010

   Bib PDF

   @inproceedings{CJ:ISMIR-2010,
     author = {Joder, Cyril and Essid, Slim and Richard, Gael},
     title = {An Improved Hierarchical Approach for Music-to-Symbolic Score Alignment},
     booktitle = {International Conference on Music Information Retrieval (ISMIR)},
     year = {2010},
     address = {Utrecht, The Netherlands},
     month = aug,
     annote = {category=inproceedings state=toappear project=audiosig dept=tsi group=aao
     	id=10383},
     file = {:D\:\\documents\\myPage\\papers\\CJ_ISMIR-10.pdf:PDF},
     owner = {essid},
     timestamp = {2010.06.24}
   }

6. YAAFE, AN EASY TO USE AND EFFICIENT AUDIO FEATURE EXTRACTION SOFTWARE

   B. Mathieu , S. Essid, T. Fillon , J. Prado , and G. Richard

   In International Conference on Music Information Retrieval (ISMIR) , Aug 2010

   Bib PDF

   @inproceedings{BM:ISMIR10,
     author = {Mathieu, B. and Essid, Slim and Fillon, Thomas and Prado, J. and Richard, Gael},
     title = {YAAFE, AN EASY TO USE AND EFFICIENT AUDIO FEATURE EXTRACTION SOFTWARE},
     booktitle = {International Conference on Music Information Retrieval (ISMIR)},
     year = {2010},
     address = {Utrecht, The Netherlands},
     month = aug,
     annote = {category=inproceedings state=toappear project=audiosig dept=tsi group=aao
     	id=10395},
     file = {:D\:\\documents\\myPage\\papers\\BM_ISMIR-10.pdf:PDF},
     owner = {essid},
     timestamp = {2010.06.24}
   }

7. DESCRIPTEURS VISUELS ROBUSTES POUR L IDENTIFICATION DE LOCUTEURS DANS DES EMISSIONS TELEVISEES DE TALK-SHOWS

   F. Vallet , S. Essid, J. Carrive , and G. Richard

   In Compression et Représentation des Signaux Audiovisuels (CORESA) , Oct 2010

   Bib PDF

   @inproceedings{FV:CORESA-10,
     author = {Vallet, F. and Essid, Slim and Carrive, J. and Richard, Gael},
     title = {Descripteurs visuels robustes pour l identification de locuteurs
     	dans des emissions televisees de talk-shows},
     booktitle = {Compression et Repr\ésentation des Signaux Audiovisuels (CORESA)},
     year = {2010},
     address = {Lyon, France},
     month = oct,
     annote = {category=inproceedings state=toappear project=audiosig dept=tsi group=aao
     	id=10393},
     file = {:D\:\\documents\\myPage\\papers\\FV_CORESA-10.pdf:PDF},
     owner = {essid},
     timestamp = {2010.06.24}
   }

8. ROBUST VISUAL FEATURES FOR THE MULTIMODAL IDENTIFICATION OF UNREGISTERED SPEAKERS IN TV TALK-SHOWS

   F. Vallet , S. Essid, J. Carrive , and G. Richard

   In IEEE International Conference on Image Processing (ICIP) , Oct 2010

   Bib PDF

   @inproceedings{FV:ICIP10,
     author = {Vallet, F. and Essid, Slim and Carrive, J. and Richard, Gael},
     title = {ROBUST VISUAL FEATURES FOR THE MULTIMODAL IDENTIFICATION OF UNREGISTERED
     	SPEAKERS IN TV TALK-SHOWS},
     booktitle = {IEEE International Conference on Image Processing (ICIP)},
     year = {2010},
     month = oct,
     annote = {category=inproceedings state=toappear project=audiosig dept=tsi group=aao
     	id=10393},
     file = {:./papers/FV_ICIP-10.pdf:PDF},
     owner = {essid},
     timestamp = {2010.06.24}
   }

2009

1. INTERACTIVE SEGMENTATION OF ELECTRO-ACOUSTIC MUSIC

   S. Gulluni , S. Essid, O. Buisson , E. Favreau , and G. Richard

   In 2nd International Workshop on Machine Learning and Music (MML - ECML - PKDD) , Sep 2009

   Bib PDF

   @inproceedings{SG:MML-09,
     author = {Gulluni, S. and Essid, Slim and Buisson, O. and Favreau, E. and Richard, Gael},
     title = {Interactive Segmentation of Electro-Acoustic Music},
     booktitle = {2nd International Workshop on Machine Learning and Music (MML - ECML
     	- PKDD)},
     year = {2009},
     address = {Bled, Slovenia},
     month = sep,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao id=10303},
     file = {SG_MML-09.pdf:./papers/SG_MML-09.pdf:PDF},
     owner = {essid},
     timestamp = {2010.03.10}
   }

2. ETUDE DES DESCRIPTEURS ACOUSTIQUES POUR L ALIGNEMENT TEMPOREL AUDIO-SUR-PARTITION MUSICALE

   C. Joder , S. Essid, and G. Richard

   In GRETSI , Sep 2009

   Bib PDF

   @inproceedings{CJ:GRETSI-09,
     author = {Joder, Cyril and Essid, Slim and Richard, Gael},
     title = {Etude des descripteurs acoustiques pour l alignement temporel audio-sur-partition
     	musicale},
     booktitle = {GRETSI},
     year = {2009},
     address = {Dijon},
     month = sep,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao id=9946},
     file = {CJ_GRETSI-09.pdf:./papers/CJ_GRETSI-09.pdf:PDF},
     owner = {essid},
     timestamp = {2010.03.10}
   }

3. TEMPORAL INTEGRATION FOR AUDIO CLASSIFICATION WITH APPLICATION TO MUSICAL INSTRUMENT CLASSIFICATION

   C. Joder , S. Essid, and G. Richard

   IEEE Transactions on Audio, Speech and Language Processing, Jan 2009

   Bib PDF

   @article{CJ:TASLP-08,
     author = {Joder, Cyril and Essid, Slim and Richard, Gael},
     title = {Temporal Integration for Audio Classification with Application to
     	Musical Instrument Classification},
     journal = {IEEE Transactions on Audio, Speech and Language Processing},
     year = {2009},
     volume = {17},
     pages = {174-186},
     number = {1},
     month = jan,
     annote = {category=article state=published doi=10.1109/TASL.2008.2007613 project=audiosig
     	dept=tsi group=aao documentURL=http://www.tsi.enst.fr/publications/enst/article-2009-8585.pdf
     	id=8585},
     file = {:./papers/CJ_TASLP-09.pdf:PDF},
     url = {http://www.tsi.enst.fr/publications/enst/article-2009-8585.pdf}
   }

4. INCORPORATING PRIOR KNOWLEDGE ON THE DIGITAL MEDIA CREATION PROCESS INTO AUDIO CLASSIFIERS

   M. Lardeur , S. Essid, G. Richard , M. Haller , and T. Sikora

   In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Apr 2009

   Bib PDF

   @inproceedings{MaxL:ICASSP09,
     author = {Lardeur, M. and Essid, Slim and Richard, Gael and Haller, M. and Sikora, T.},
     title = {Incorporating prior knowledge on the digital media creation process
     	into audio classifiers},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing
     	(ICASSP)},
     year = {2009},
     address = {Taipei, Taiwan},
     month = apr,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao id=9336},
     file = {MaxL_ICASSP09.pdf:./papers/ML_ICASSP-09.pdf:PDF},
   }

5. RECONNAISSANCE DES INSTRUMENTS DANS LA MUSIQUE POLYPHONIQUE PAR DÉCOMPOSITION NMF ET CLASSIFICATION SVM

   A. Ozerov , S. Essid, and M. Charbit

   Apr 2009

   Bib PDF

   @techreport{AO:TREP-09,
     author = {Ozerov, Alexey and Essid, Slim and Charbit, M.},
     title = {Reconnaissance des instruments dans la musique polyphonique par d\écomposition
     	NMF et classification SVM},
     institution = {TELECOM ParisTech},
     year = {2009},
     number = {2009D014},
     file = {AO_TREP-09.pdf:./papers/AO_TREP-09.pdf:PDF},
     owner = {essid},
     timestamp = {2009.08.11}
   }

2008

1. A COLLABORATIVE APPROACH TO AUTOMATIC RUSHES VIDEO SUMMARIZATION

   W. Bailer , E. Dumont , S. Essid, and B. Mérialdo

   In IEEE ICIP Workshop on Multimedia Information Retrieval: New Trends and Challenges , Oct 2008

   Bib PDF

   @inproceedings{SE:ICIP-08,
     author = {Bailer, W. and Dumont, E. and Essid, Slim and M\érialdo, B.},
     title = {A Collaborative Approach to Automatic Rushes Video Summarization},
     booktitle = {IEEE ICIP Workshop on Multimedia Information Retrieval: New Trends
     	and Challenges},
     year = {2008},
     month = oct,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao id=8679},
     file = {SE_ICIP-08.pdf:./papers/SE_ICIP-08.pdf:PDF},
   }

2. A COLLABORATIVE APPROACH TO VIDEO SUMMARIZATION

   E. Dumont , B. Merialdo , S. Essid, W. Bailer , D. Byrne , H. Bredin , N. OConnor , G. Jones , M. Haller , A. Krutz , T. Sikora , and T. Piatrik

   In 3rd International Conference on Semantic and Digital Media Technologies (SAMT) , Dec 2008

   Bib PDF

   @inproceedings{SE:SAMT-08,
     author = {Dumont, E. and Merialdo, B. and Essid, Slim and Bailer, W. and Byrne, D. and Bredin, H. and OConnor, N. E. and Jones, G. J. F. and Haller, M. and Krutz, A. and Sikora, T. and Piatrik, T.},
     title = {A collaborative approach to video summarization},
     booktitle = {3rd International Conference on Semantic and Digital Media Technologies
     	(SAMT)},
     year = {2008},
     address = {Koblenz, Germany},
     month = dec,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao id=8680},
     file = {SE_SAMT-08.pdf:./papers/SE_SAMT-08.pdf:PDF},
   }

3. RUSHES VIDEO SUMMARIZATION USING A COLLABORATIVE APPROACH

   E. Dumont , B. Merialdo , S. Essid, W. Bailer , H. Rehatschek , D. Byrne , H. Bredin , N. OConnor , G. Jones , A. Smeaton , . M. Haller , A. Krutz , T. Sikora , and T. Piatrik

   In TRECVID 2008, ACM International Conference on Multimedia Information Retrieval 2008 , Nov 2008

   Bib PDF

   @inproceedings{SE:TRECVID-08,
     author = {Dumont, E. and Merialdo, B. and Essid, Slim and Bailer, W. and Rehatschek, H. and Byrne, D. and Bredin, H. and OConnor, N. E. and Jones, G. J. F. and Smeaton, A. F. and and M. Haller and Krutz, A. and Sikora, T. and Piatrik, T.},
     title = {Rushes video summarization using a collaborative approach},
     booktitle = {TRECVID 2008, ACM International Conference on Multimedia Information
     	Retrieval 2008},
     year = {2008},
     address = {Vancouver, BC, Canada},
     month = nov,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao id=8681},
     file = {SE_TRECVID-08.pdf:./papers/SE_ACM-08.pdf:PDF},
   }

4. ALIGNMENT KERNELS FOR AUDIO CLASSIFICATION WITH APPLICATION TO MUSIC INSTRUMENT RECOGNITION

   C. Joder , S. Essid, and G. Richard

   In European Signal Processing Conference (EUSIPCO) , Aug 2008

   Bib PDF

   @inproceedings{CJ-EUSIPCO-2008,
     author = {Joder, Cyril and Essid, Slim and Richard, Gael},
     title = {Alignment Kernels for Audio Classification with Application to Music
     	Instrument Recognition},
     booktitle = {European Signal Processing Conference (EUSIPCO)},
     year = {2008},
     address = {Lausanne, Suisse},
     month = aug,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao documentURL=http://www.tsi.enst.fr/publications/enst/inproceedings-2008-8528.pdf
     	id=8528},
     file = {:./papers/CJ_EUSIPCO-08.pdf:PDF},
     url = {http://www.tsi.enst.fr/publications/enst/inproceedings-2008-8528.pdf}
   }

5. ON THE ROBUSTNESS OF AUDIO FEATURES FOR MUSICAL INSTRUMENT CLASSIFICATION

   S. Wegener , M. Haller , J. Burred , T. Sikora , S. Essid, and G. Richard

   In European Signal Processing Conference (EUSIPCO) , Sep 2008

   Bib PDF

   @inproceedings{SW:EUSIPCO08,
     author = {Wegener, S. and Haller, M. and Burred, J.-J. and Sikora, T. and Essid, Slim and Richard, Gael},
     title = {On the Robustness of Audio Features for Musical Instrument Classification},
     booktitle = {European Signal Processing Conference (EUSIPCO)},
     year = {2008},
     address = {Lausanne, Switzerland},
     month = sep,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao id=8616},
     file = {SW_EUSIPCO08.pdf:./papers/SW_EUSIPCO08.pdf:PDF},
   }

2007

1. ON THE CORRELATION OF AUTOMATIC AUDIO AND VISUAL SEGMENTATIONS OF MUSIC VIDEOS

   O. Gillet , S. Essid, and G. Richard

   IEEE Transactions on Circuits and Systems for Video Technology, Mar 2007

   Bib PDF

   @article{OG:CVST-07,
     author = {Gillet, O. and Essid, Slim and Richard, Gael},
     title = {On the Correlation of Automatic Audio and Visual Segmentations of
     	Music Videos},
     journal = {IEEE Transactions on Circuits and Systems for Video Technology},
     year = {2007},
     month = mar,
     annote = {category=article state=published project=audiosig dept=tsi group=aao
     	id=6864},
     file = {OG_CVST-07.pdf:./papers/OG_TCSVT-07.pdf:PDF},
   }

2. TOWARDS POLYPHONIC MUSICAL INSTRUMENT RECOGNITION

   G. Richard , P. Leveau , L. Daudet , S. Essid, and B. David

   In International Congress on Acoustics (ICA) , Sep 2007

   Bib PDF

   @inproceedings{ICA:07,
     author = {Richard, Gael and Leveau, P. and Daudet, L. and Essid, Slim and David, Bertrand},
     title = {Towards polyphonic musical instrument recognition},
     booktitle = {International Congress on Acoustics (ICA)},
     year = {2007},
     address = {Madrid},
     month = sep,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao id=7332},
     file = {ICA_07.pdf:./papers/GR_ICA-07.pdf:PDF},
   }

3. COMBINED SUPERVISED AND UNSUPERVISED APPROACHES FOR AUTOMATIC SEGMENTATION OF RADIOPHONIC AUDIO STREAMS

   G. Richard , M. Ramona , and S. Essid

   In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Apr 2007

   Bib PDF

   @inproceedings{GR:Icassp-07,
     author = {Richard, Gael and Ramona, M. and Essid, Slim},
     title = {Combined supervised and unsupervised approaches for automatic segmentation
     	of radiophonic audio streams},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing
     	(ICASSP)},
     year = {2007},
     address = {Honolulu, Hawai},
     month = apr,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao id=6862},
     file = {GR_Icassp-07.pdf:./papers/GR_ICASSP-07.pdf:PDF},
   }

4. K-SPACE AT TRECVID 2007

   P. Wilkins , T. Adamek , D. Byrne , G. Jones , H. Lee , G. Keenan , K. Guinness , N. OConnor , A. Smeaton , A. Amin , Z. Obrenovic , R. Benmokhtar , E. Galmar , B. Huet , S. Essid, R. Landais , F. Vallet , G. Papadopoulos , S. Vrochidis , V. Mezaris , I. Kompatsiaris , E. Spyrou , Y. Avrithis , R. Morzinger , P. Schallauer , W. Bailer , T. Piatrik , K. Chandramouli , E. Izquierdo , M. Haller , L. Goldmann , A. Samour , A. Cobet , T. Sikora , and P. Praks

   In TRECVID 2007 , Nov 2007

   Bib PDF

   @inproceedings{SE:TRECVID-07,
     author = {Wilkins, P. and Adamek, T. and Byrne, D. and Jones, G. J. F. and Lee, H. and Keenan, G. and Guinness, K. Mc and OConnor, N. E. and Smeaton, A. F. and Amin, A. and Obrenovic, Z. and Benmokhtar, R. and Galmar, E. and Huet, B. and Essid, Slim and Landais, R. and Vallet, F. and Papadopoulos, G. T. and Vrochidis, S. and Mezaris, V. and Kompatsiaris, I. and Spyrou, E. and Avrithis, Y. and Morzinger, R. and Schallauer, P. and Bailer, W. and Piatrik, T. and Chandramouli, K. and Izquierdo, E. and Haller, M. and Goldmann, L. and Samour, A. and Cobet, A. and Sikora, T. and Praks, P.},
     title = {K-Space at TRECVid 2007},
     booktitle = {TRECVID 2007},
     year = {2007},
     month = nov,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao documentURL=http://www.cdvp.dcu.ie/Papers/kspace-tv2007.pdf
     	id=7755},
     file = {:./papers/SE_TRECVID-07.pdf:PDF},
     url = {http://www.cdvp.dcu.ie/Papers/kspace-tv2007.pdf}
   }

2006

1. INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC BASED ON AUTOMATIC TAXONOMIES

   S. Essid, G. Richard , and B. David

   IEEE Transactions on Audio, Speech, and Language Processing, Jan 2006

   Bib PDF

   @article{SE:COD-06,
     author = {Essid, Slim and Richard, Gael and David, Bertrand},
     title = {Instrument recognition in polyphonic music based on automatic taxonomies},
     journal = {IEEE Transactions on Audio, Speech, and Language Processing},
     year = {2006},
     volume = {14},
     pages = {68-80},
     number = {1},
     month = jan,
     annote = {category=article state=published doi=10.1109/TSA.2005.860351 project=audiosig
     	dept=tsi group=aao id=5909},
     file = {SE_COD-06.pdf:./papers/SE_TSALP-06b.pdf:PDF},
   }

2. MUSICAL INSTRUMENT RECOGNITION BY PAIRWISE CLASSIFICATION STRATEGIES

   S. Essid, G. Richard , and B. David

   IEEE Transactions on Audio, Speech, and Language Processing, Jul 2006

   Bib PDF

   @article{SE:COD-06-2,
     author = {Essid, Slim and Richard, Gael and David, Bertrand},
     title = {Musical instrument recognition by pairwise classification strategies},
     journal = {IEEE Transactions on Audio, Speech, and Language Processing},
     year = {2006},
     volume = {14},
     pages = {1401- 1412},
     number = {4},
     month = jul,
     annote = {category=article state=published doi=10.1109/TSA.2005.860842 project=audiosig
     	dept=tsi group=aao id=5910},
     file = {SE_COD-06-2.pdf:./papers/SE_TSALP-06a.pdf:PDF},
   }

3. HIERARCHICAL CLASSIFICATION OF MUSICAL INSTRUMENTS ON SOLO RECORDINGS

   S. Essid, G. Richard , and B. David

   In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , May 2006

   Bib PDF

   @inproceedings{SE:ICASSP-06,
     author = {Essid, Slim and Richard, Gael and David, Bertrand},
     title = {Hierarchical Classification of Musical Instruments on Solo Recordings},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing
     	(ICASSP)},
     year = {2006},
     address = {Toulouse, France},
     month = may,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao id=6672},
     file = {SE_ICASSP-06.pdf:./papers/SE_ICASSP-06.pdf:PDF},
   }

2005

1. CLASSIFICATION AUTOMATIQUE DES SIGNAUX AUDIO-FRÉQUENCES: RECONNAISSANCE DES INSTRUMENTS DE MUSIQUE

   S. Essid

   Université Pierre et Marie Curie , Dec 2005

   Bib PDF

   @phdthesis{SE:these,
     author = {Essid, Slim},
     title = {Classification automatique des signaux audio-fr\équences:
     	reconnaissance des instruments de musique},
     school = {Universit\é Pierre et Marie Curie},
     year = {2005},
     month = dec,
     annote = {category=phdthesis state=published project=audiosig dept=tsi group=aao
     	id=7638},
     file = {SE_these.pdf:./papers/SE_PhD-05.pdf:PDF},
   }

2. INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC

   S. Essid, G. Richard , and B. David

   In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , Mar 2005

   Bib PDF

   @inproceedings{SE-ICASSP-05,
     author = {Essid, Slim and Richard, Gael and David, Bertrand},
     title = {Instrument recognition in polyphonic music},
     booktitle = {IEEE International Conference on Acoustics, Speech and Signal Processing
     	(ICASSP)},
     year = {2005},
     address = {Philadelphia, US},
     month = mar,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao id=5067},
     file = {SE-ICASSP-05.pdf:./papers/SE_ICASSP-05.pdf:PDF},
   }

3. ON THE USEFULNESS OF DIFFERENTIATED TRANSIENT/STEADY-STATE PROCESSING IN MACHINE RECOGNITION OF MUSICAL INSTRUMENTS

   P. Leveau , S. Essid, G. Richard , L. Daudet , and B. David

   In AES convention , May 2005

   Bib PDF

   @inproceedings{PL-AES-05,
     author = {Leveau, P. and Essid, Slim and Richard, Gael and Daudet, L. and David, Bertrand},
     title = {On the usefulness of differentiated transient/steady-state processing
     	in machine recognition of musical instruments},
     booktitle = {AES convention},
     year = {2005},
     address = {Barcelona, Spain},
     month = may,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao id=5071},
     file = {PL-AES-05.pdf:./papers/SE_AES-05.pdf:PDF},
   }

2004

1. EFFICIENT MUSICAL INSTRUMENT RECOGNITION ON SOLO PERFORMANCES USING BASIC FEATURES

   S. Essid, G. Richard , and B. David

   In AES 25th conference , Jun 2004

   Bib PDF

   @inproceedings{SE:AES-04,
     author = {Essid, Slim and Richard, Gael and David, Bertrand},
     title = {Efficient musical instrument recognition on solo performances using
     	basic features},
     booktitle = {AES 25th conference},
     year = {2004},
     address = {London, UK},
     month = jun,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao,cod id=3608},
     file = {SE_AES-04.pdf:./papers/SE_AES-04.pdf:PDF},
   }

2. MUSICAL INSTRUMENT RECOGNITION ON SOLO PERFORMANCES

   S. Essid, G. Richard , and B. David

   In European Signal Processing Conference (EUSIPCO) , Sep 2004

   Bib PDF

   @inproceedings{SE:Eusipco-04,
     author = {Essid, Slim and Richard, Gael and David, Bertrand},
     title = {Musical instrument recognition on solo performances},
     booktitle = {European Signal Processing Conference (EUSIPCO)},
     year = {2004},
     address = {Vienna, Austria},
     month = sep,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao,cod documentURL=http://www.enst.fr/~grichard/publications.htm
     	id=4893},
     file = {:D\:\\documents\\myPage\\papers\\SE_EUSIPCO-04.pdf:PDF},
     url = {http://www.enst.fr/~grichard/publications.htm}
   }

3. MUSICAL INSTRUMENT RECOGNITION BASED ON CLASS PAIRWISE FEATURE SELECTION

   S. Essid, G. Richard , and B. David

   In International Conference on Music Information Retrieval (ISMIR) , Oct 2004

   Bib PDF

   @inproceedings{SE:ISMIR-04,
     author = {Essid, Slim and Richard, Gael and David, Bertrand},
     title = {Musical instrument recognition based on class pairwise feature selection},
     booktitle = {International Conference on Music Information Retrieval (ISMIR)},
     year = {2004},
     address = {Barcelona, Spain},
     month = oct,
     annote = {category=inproceedings state=published project=audiosig dept=tsi
     	group=aao,cod documentURL=http://www.enst.fr/~grichard/publications.htm
     	id=4892},
     file = {:./papers/SE_ISMIR-04.pdf:PDF},
     url = {http://www.enst.fr/~grichard/publications.htm}
   }

2003

1. MODÈLES SINUSOÏDAUX ÉTENDUS POUR LE CODAGE AUDIO

   R. Boyer , S. Essid, K. Abed-Meraim , and N. Moreau

   In Dix-neuvième colloque sur le Traitement du Signal et des Images , Sep 2003

   Bib PDF

   @inproceedings{SE:GRETSI-03,
     author = {Boyer, R. and Essid, Slim and Abed-Meraim, K. and Moreau, N.},
     title = {Mod\èles sinuso\ïdaux \étendus pour le codage
     	audio},
     booktitle = {Dix-neuvi\ème colloque sur le Traitement du Signal et des
     	Images},
     year = {2003},
     address = {Paris, France},
     month = sep,
     annote = {category=inproceedings state=published project= dept=tsi group=aao,cod,tsac
     	id=3453},
     file = {SE_GRETSI-03.pdf:./papers/RB_GRETSI-03.pdf:PDF},
   }

2002

1. TRANSIENT MODELING WITH A FREQUENCY-TRANSFORM SUBSPACE ALGORITHM AND TRANSIENT + SINUSOIDAL SCHEME

   R. Boyer , and S. Essid

   In 14th IEEE Int. Conf. on Digital Signal Proc. , Jul 2002

   Abs Bib PDF

   We present an efficient modeling method for strong transient character audio signals. It is shown that the parametric non-stationary exponentially damped sinusoids (EDS) model permits good performance for time domain modeling of quasi-stationary signals or "weak" transients. However, a decay in modeling performance is observed when dealing with highly nonstationary signals as in a variety of musical sounds (various percussions, castanets, triangle,...). The idea is then to process the signal in a well chosen frequency-transform domain in which the transient temporal characteristics are better modeled by EDS. As a result, better representations of the transient signal class are obtained with no pre-echo artifacts (energy before the attack) and a very good signal onset dynamic reproduction. Finally, an original "transient+sinusoidal" modeling scheme is proposed.

   @inproceedings{RB:DSP-02,
     author = {Boyer, R. and Essid, Slim},
     title = {Transient modeling with a Frequency-Transform Subspace Algorithm
     	and Transient + Sinusoidal scheme},
     booktitle = {14th IEEE Int. Conf. on Digital Signal Proc.},
     year = {2002},
     address = {Santorini (Greece)},
     month = jul,
     annote = {category=inproceedings state=published project= dept=tsi group=cod
     	id=2014},
     file = {RB_DSP-02.pdf:./papers/RB_DSP-02.pdf:PDF},
   }

2. DYNAMIC TEMPORAL SEGMENTATION IN PARAMETRIC NON-STATIONARY MODELING FOR PERCUSSIVE MUSICAL SIGNALS

   R. Boyer , S. Essid, and N. Moreau

   In IEEE Int. Conf. on Multimedia and Expo (ICME) , Aug 2002

   Abs Bib PDF

   An audio signal parametric modeling scheme is proposed that permits higher performance for representing strong sound transients. The exponentially damped sinusoids (EDS) model is considered in association with a high resolution parameter estimation approach. Such a technique is well adapted to almost every audio signal but is unfortunately not efficient when dealing with signals presenting strong temporal variations, such as percussive music signals, and causes pre-echo artifacts and weak onset dynamic reproduction which are prejudicial to listening. A system, based on the EDS model, has been developed with a transient detector and dynamic time segmentation and modeling that allows to overcome such artifacts.

   @inproceedings{RB:ICM-02,
     author = {Boyer, R. and Essid, Slim and Moreau, N.},
     title = {Dynamic temporal segmentation in parametric non-stationary modeling
     	for percussive musical signals},
     booktitle = {IEEE Int. Conf. on Multimedia and Expo (ICME)},
     year = {2002},
     address = {Lausanne, Switzerland},
     month = aug,
     annote = {category=inproceedings state=published project= dept=tsi group=cod
     	id=2013},
     file = {RB_ICM-02.pdf:./papers/RB_ICM-02.pdf:PDF},
   }

3. NON-STATIONARY SIGNAL PARAMETRIC MODELING TECHNIQUES WITH AN APPLICATION TO LOW BITRATE AUDIO CODING

   R. Boyer , S. Essid, and N. Moreau

   In 6th IEEE Int. Conf. Signal Processing , Aug 2002

   Abs Bib PDF

   Low bit rate audio coding often relies on Fourier representation despite its limitations for transient signal modeling. This study proposes alternative decompositions and expansion strategies that lead to more accurate modeling. Two classes of methods are considered, subspace decomposition methods, and atomic decomposition methods and their performances are compiled to propose an audio modeling scheme amenable to low bit rate coding.

   @inproceedings{RB:ICS-02,
     author = {Boyer, R. and Essid, Slim and Moreau, N.},
     title = {Non-stationary signal parametric modeling techniques with an application
     	to low bitrate audio coding},
     booktitle = {6th IEEE Int. Conf. Signal Processing},
     year = {2002},
     address = {Beijing, China},
     month = aug,
     annote = {category=inproceedings state=published project= dept=tsi group=cod
     	id=2012},
     file = {RB_ICS-02.pdf:./papers/RB_ICS-02.pdf:PDF},
   }

4. CODEUR AUDIO PARAMÉTRIQUE BAS DÉBIT BASÉ SUR UN MODÈLE "SINUSOÏDES AMORTIES EXPONENTIELLEMENT + TRANSITOIRES + BRUIT"

   S. Essid

   Ecole Nationale Supérieure des Télécommunications (ENST) , Oct 2002

   Bib PDF

   @mastersthesis{SE:master-02,
     author = {Essid, Slim},
     title = {Codeur audio param\étrique bas d\ébit bas\é
     	sur un mod\èle "Sinuso\ïdes Amorties Exponentiellement
     	+ Transitoires + Bruit"},
     school = {Ecole Nationale Sup\érieure des T\él\écommunications
     	(ENST)},
     year = {2002},
     month = oct,
     file = {dea.pdf:./papers/SE_MastTh-02.pdf:PDF},
     owner = {essid},
     timestamp = {2009.08.11}
   }

2001

1. EXPLORATION DE TECHNIQUES MODERNES DE MODÉLISATION ADAPTÉES À DU CODAGE AUDIO BAS-DÉBIT

   R. Boyer , S. Essid, and N. Moreau

   In 7èmes Journées d Etudes et d Echanges : Compression et Représentation des Signaux Audiovisuels (CORESA) , Oct 2001

   Bib PDF

   @inproceedings{Boyer_01,
     author = {Boyer, R. and Essid, Slim and Moreau, N.},
     title = {Exploration de techniques modernes de mod\élisation adapt\ées
     	\à du codage audio bas-d\ébit},
     booktitle = {7\èmes Journ\ées d Etudes et d Echanges : Compression
     	et Repr\ésentation des Signaux Audiovisuels (CORESA)},
     year = {2001},
     address = {Dijon, France},
     month = oct,
     annote = {category=inproceedings state=published project= dept=tsi group=cod
     	id=1971},
     file = {RB_CORESA-01.pdf:./papers/RB_CORESA-01.pdf:PDF},
   }