514 citations · 586 across the 44 of their papers we have counts for
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Resource-Efficient Separation Transformer
Luca Della Libera, Cem Subakan, Mirco Ravanelli +3
Transformers have recently achieved state-of-the-art performance in speech separation. These models, however, are computationally demanding and require a lot of learnable parameter…
Learning Representations for New Sound Classes With Continual Self-Supervised Learning
Zhepei Wang, Cem Subakan, Xilin Jiang +4
In this paper, we work on a sound recognition system that continually incorporates new sound classes. Our main goal is to develop a framework where the model can be updated without…
Exploring Self-Attention Mechanisms for Speech Separation
Cem Subakan, Mirco Ravanelli, Samuele Cornell +2
Transformers have enabled impressive improvements in deep learning. They often outperform recurrent and convolutional models in many tasks while taking advantage of parallel proces…