| Score informed audio source separation using a parametric model of non-negative spectrogram R Hennequin, B David, R Badeau 2011 IEEE International Conference on Acoustics, Speech and Signal …, 2011 | 111 | 2011 |
| Identification of cascade of Hammerstein models for the description of nonlinearities in vibrating devices M Rébillat, R Hennequin, E Corteel, BFG Katz Journal of sound and vibration 330 (5), 1018-1038, 2011 | 83 | 2011 |
| Singing voice detection with deep recurrent neural networks S Leglaive, R Hennequin, R Badeau 2015 IEEE International conference on acoustics, speech and signal …, 2015 | 79 | 2015 |
| NMF with time–frequency activations to model nonstationary audio events R Hennequin, R Badeau, B David IEEE Transactions on Audio, Speech, and Language Processing 19 (4), 744-753, 2010 | 76 | 2010 |
| Time-dependent parametric and harmonic templates in non-negative matrix factorization R Hennequin, R Badeau, B David Proc. of the 13th International Conference on Digital Audio Effects (DAFx), 2010 | 46 | 2010 |
| Beta-divergence as a subclass of Bregman divergence R Hennequin, B David, R Badeau IEEE Signal Processing Letters 18 (2), 83-86, 2010 | 39 | 2010 |
| Spleeter: A fast and state-of-the art music source separation tool with pre-trained models R Hennequin, A Khlif, F Voituret, M Moussallam Late-Breaking/Demo ISMIR 2019, 2019 | 38 | 2019 |
| Music mood detection based on audio and lyrics with deep neural net R Delbouys, R Hennequin, F Piccoli, J Royo-Letelier, M Moussallam ISMIR 2018, 2018 | 34 | 2018 |
| WASABI: A two million song database project with audio and cultural metadata plus WebAudio enhanced client applications G Meseguer-Brocal, G Peeters, G Pellerin, M Buffa, E Cabrio, CF Zucker, ... Web Audio Conference 2017–Collaborative Audio# WAC2017, 2017 | 22 | 2017 |
| Gravity-inspired graph autoencoders for directed link prediction G Salha, S Limnios, R Hennequin, VA Tran, M Vazirgiannis Proceedings of the 28th ACM International Conference on Information and …, 2019 | 19 | 2019 |
| Spleeter: a fast and efficient music source separation tool with pre-trained models R Hennequin, A Khlif, F Voituret, M Moussallam Journal of Open Source Software 5 (50), 2154, 2020 | 18 | 2020 |
| Keep it simple: Graph autoencoders without graph convolutional networks G Salha, R Hennequin, M Vazirgiannis arXiv preprint arXiv:1910.00942, 2019 | 16 | 2019 |
| A degeneracy framework for scalable graph autoencoders G Salha, R Hennequin, VA Tran, M Vazirgiannis arXiv preprint arXiv:1902.08813, 2019 | 16 | 2019 |
| Speech-guided source separation using a pitch-adaptive guide signal model R Hennequin, JJ Burred, S Maller, P Leveau 2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014 | 15 | 2014 |
| Prediction of harmonic distortion generated by electro-dynamic loudspeakers using cascade of Hammerstein models M Rébillat, R Hennequin, E Corteel, B Katz 128th Convention of the audio engineering society, 7993, 2010 | 15 | 2010 |
| Singing voice separation: A study on training data L Prétet, R Hennequin, J Royo-Letelier, A Vaglio ICASSP 2019-2019 ieee international conference on acoustics, speech and …, 2019 | 13 | 2019 |
| Codec independent lossy audio compression detection R Hennequin, J Royo-Letelier, M Moussallam 2017 IEEE International Conference on Acoustics, Speech and Signal …, 2017 | 12 | 2017 |
| Décomposition de spectrogrammes musicaux informée par des modeles de synthese spectrale. Modélisation des variations temporelles dans les éléments sonores. R Hennequin Télécom ParisTech, 2011 | 12 | 2011 |
| Audio based disambiguation of music genre tags R Hennequin, J Royo-Letelier, M Moussallam ISMIR 2018, 2018 | 10 | 2018 |
| Scale-invariant probabilistic latent component analysis R Hennequin, R Badeau, B David 2011 IEEE Workshop on Applications of Signal Processing to Audio and …, 2011 | 10 | 2011 |
Manuel MoussallamDeezer ResearchAdresse e-mail validée de deezer.com
