| SpeechBrain: A general-purpose speech toolkit M Ravanelli, T Parcollet, P Plantinga, A Rouhe, S Cornell, L Lugosch, ... arXiv preprint arXiv:2106.04624, 2021 | 615 | 2021 |
| Attention is all you need in speech separation C Subakan, M Ravanelli, S Cornell, M Bronzi, J Zhong ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 513 | 2021 |
| Generative adversarial source separation YC Subakan, P Smaragdis 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 86 | 2018 |
| Two-step sound source separation: Training on learned latent targets E Tzinis, S Venkataramani, Z Wang, C Subakan, P Smaragdis ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 83 | 2020 |
| Diagonal RNNs in symbolic music modeling YC Subakan, P Smaragdis 2017 IEEE Workshop on Applications of Signal Processing to Audio and …, 2017 | 24 | 2017 |
| Continual learning of new sound classes using generative replay Z Wang, C Subakan, E Tzinis, P Smaragdis, L Charlin 2019 IEEE Workshop on Applications of Signal Processing to Audio and …, 2019 | 22 | 2019 |
| Speechbrain M Ravanelli, T Parcollet, A Rouhe, P Plantinga, E Rastorgueva, ... GitHub repository, 2021 | 21 | 2021 |
| SpeechBrain: A general-purpose speech toolkit. arXiv 2021 M Ravanelli, T Parcollet, P Plantinga, A Rouhe, S Cornell, L Lugosch, ... arXiv preprint arXiv:2106.04624, 0 | 21 | |
| Neural network alternatives toconvolutive audio models for source separation S Venkataramani, C Subakan, P Smaragdis 2017 IEEE 27th International Workshop on Machine Learning for Signal …, 2017 | 20 | 2017 |
| Spectral learning of mixture of hidden markov models C Subakan, J Traa, P Smaragdis Advances in Neural Information Processing Systems 27, 2014 | 20 | 2014 |
| On using transformers for speech-separation C Subakan, M Ravanelli, S Cornell, F Grondin, M Bronzi arXiv preprint arXiv:2202.02884, 2022 | 18 | 2022 |
| SpeechBrain: a general-purpose speech toolkit. arXiv M Ravanelli, T Parcollet, P Plantinga, A Rouhe, S Cornell, L Lugosch, ... arXiv preprint arXiv:2106.04624 10, 2021 | 18 | 2021 |
| Exploring self-attention mechanisms for speech separation C Subakan, M Ravanelli, S Cornell, F Grondin, M Bronzi IEEE/ACM Transactions on Audio, Speech, and Language Processing 31, 2169-2180, 2023 | 17 | 2023 |
| REAL-M: Towards speech separation on real mixtures C Subakan, M Ravanelli, S Cornell, F Grondin ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 17 | 2022 |
| Probabilistic latent tensor factorization framework for audio modeling AT Cemgil, U Şimşekli, YC Sübakan 2011 IEEE Workshop on Applications of Signal Processing to Audio and …, 2011 | 16 | 2011 |
| SpeechBrain: a general-purpose speech toolkit (2021) M Ravanelli, T Parcollet, P Plantinga, A Rouhe, S Cornell, L Lugosch, ... arXiv preprint arXiv:2106.04624, 2022 | 15 | 2022 |
| Learning representations for new sound classes with continual self-supervised learning Z Wang, C Subakan, X Jiang, J Wu, E Tzinis, M Ravanelli, P Smaragdis IEEE Signal Processing Letters 29, 2607-2611, 2022 | 14 | 2022 |
| Probabilistic sequence clustering with spectral learning YC Sübakan, B Kurt, AT Cemgil, B Sankur Digital Signal Processing 29, 1-19, 2014 | 14 | 2014 |
| Method of moments learning for left-to-right hidden Markov models YC Subakan, J Traa, P Smaragdis, D Hsu 2015 IEEE workshop on applications of signal processing to audio and …, 2015 | 13 | 2015 |
| Resource-efficient separation transformer L Della Libera, C Subakan, M Ravanelli, S Cornell, F Lepoutre, F Grondin arXiv preprint arXiv:2206.09507, 2022 | 12 | 2022 |
Mirco RavanelliConcordia University, Université de Montréal, Mila Quebec AI Institute在 concordia.ca 的电子邮件经过验证
