Cornell, S., Olvera, M., Pariente, M., Pepe, G., Principi, E., Gabrielli, L., & Squartini, S. (2020, November). Task-Aware Separation for the DCASE 2020 Task 4 Sound Event Detection and Separation Challenge. In DCASE 2020-5th Workshop on Detection and Classification of Acoustic Scenes and Events. [PDF]
Cornell, S., Olvera, M., Pariente, M., Pepe, G., Principi, E., Gabrielli, L., & Squartini, S. (2020, November). Domain-adversarial training and trainable parallel front-end for the DCASE 2020 task 4 sound event detection challenge. In DCASE 2020-5th Workshop on Detection and Classification of Acoustic Scenes and Events. [PDF]
Cosentino, J., Pariente, M., Cornell, S., Deleforge, A., & Vincent, E. (2020). Librimix: An open-source dataset for generalizable speech separation. arXiv preprint arXiv:2005.11262. [PDF]
Pariente, M., Cornell, S., Cosentino, J., Sivasankaran, S., Tzinis, E., Heitkaemper, J., … & Vincent, E. (2020, October). Asteroid: the PyTorch-based audio source separation toolkit for researchers. In Interspeech. [PDF]
Pariente, M., Cornell, S., Deleforge, A., & Vincent, E. (2020, May). Filterbank design for end-to-end speech separation. In ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6364-6368). IEEE. [PDF]
2019
Pariente, M., Deleforge, A., & Vincent, E. (2019, January). A Statistically Principled and Computationally Efficient Approach to Speech Enhancement Using Variational Autoencoders. In INTERSPEECH. [PDF]