| 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 | 390 | 2020 |
| Singing voice detection with deep recurrent neural networks S Leglaive, R Hennequin, R Badeau 2015 IEEE International conference on acoustics, speech and signal …, 2015 | 120 | 2015 |
| 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 | 118 | 2011 |
| Music mood detection based on audio and lyrics with deep neural net R Delbouys, R Hennequin, F Piccoli, J Royo-Letelier, M Moussallam arXiv preprint arXiv:1809.07276, 2018 | 112 | 2018 |
| 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 | 107 | 2011 |
| 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 | 102 | 2019 |
| 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 | 84 | 2010 |
| Simple and effective graph autoencoders with one-hop linear models G Salha, R Hennequin, M Vazirgiannis Machine Learning and Knowledge Discovery in Databases: European Conference …, 2021 | 58 | 2021 |
| Explainability in music recommender systems D Afchar, A Melchiorre, M Schedl, R Hennequin, E Epure, M Moussallam AI Magazine 43 (2), 190-208, 2022 | 57 | 2022 |
| Keep it simple: Graph autoencoders without graph convolutional networks G Salha, R Hennequin, M Vazirgiannis arXiv preprint arXiv:1910.00942, 2019 | 57 | 2019 |
| 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 | 52 | 2019 |
| Beta-divergence as a subclass of Bregman divergence R Hennequin, B David, R Badeau IEEE Signal Processing Letters 18 (2), 83-86, 2010 | 50 | 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 | 47 | 2010 |
| A degeneracy framework for scalable graph autoencoders G Salha, R Hennequin, VA Tran, M Vazirgiannis arXiv preprint arXiv:1902.08813, 2019 | 43 | 2019 |
| Modularity-aware graph autoencoders for joint community detection and link prediction G Salha-Galvan, JF Lutzeyer, G Dasoulas, R Hennequin, M Vazirgiannis Neural Networks 153, 474-495, 2022 | 37 | 2022 |
| Multilingual lyrics-to-audio alignment A Vaglio, R Hennequin, M Moussallam, G Richard, F d'Alché-Buc International Society for Music Information Retrieval Conference (ISMIR), 2020 | 29 | 2020 |
| Improving collaborative metric learning with efficient negative sampling VA Tran, R Hennequin, J Royo-Letelier, M Moussallam Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019 | 28 | 2019 |
| Making neural networks interpretable with attribution: application to implicit signals prediction D Afchar, R Hennequin Proceedings of the 14th ACM conference on recommender systems, 220-229, 2020 | 26 | 2020 |
| Fastgae: Scalable graph autoencoders with stochastic subgraph decoding G Salha, R Hennequin, JB Remy, M Moussallam, M Vazirgiannis Neural Networks 142, 1-19, 2021 | 24 | 2021 |
| 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, ... | 22 | 2017 |
Manuel MoussallamDeezer Research在 deezer.com 的电子邮件经过验证
