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Romain Hennequin
Romain Hennequin
Deezer Research
Подтвержден адрес электронной почты в домене deezer.com - Главная страница
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Процитировано
Процитировано
Год
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
4442020
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
1312018
Singing voice detection with deep recurrent neural networks
S Leglaive, R Hennequin, R Badeau
2015 IEEE International conference on acoustics, speech and signal …, 2015
1212015
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
1192011
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
1122011
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
1112019
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
852010
Explainability in music recommender systems
D Afchar, A Melchiorre, M Schedl, R Hennequin, E Epure, M Moussallam
AI Magazine 43 (2), 190-208, 2022
832022
Keep it simple: Graph autoencoders without graph convolutional networks
G Salha, R Hennequin, M Vazirgiannis
arXiv preprint arXiv:1910.00942, 2019
672019
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
662021
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
542019
Beta-divergence as a subclass of Bregman divergence
R Hennequin, B David, R Badeau
IEEE Signal Processing Letters 18 (2), 83-86, 2010
532010
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
492022
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
472010
A degeneracy framework for scalable graph autoencoders
G Salha, R Hennequin, VA Tran, M Vazirgiannis
arXiv preprint arXiv:1902.08813, 2019
442019
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
342020
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
302019
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
292020
Fastgae: Scalable graph autoencoders with stochastic subgraph decoding
G Salha, R Hennequin, JB Remy, M Moussallam, M Vazirgiannis
Neural Networks 142, 1-19, 2021
272021
Towards rigorous interpretations: a formalisation of feature attribution
D Afchar, V Guigue, R Hennequin
International Conference on Machine Learning, 76-86, 2021
252021
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