1 citations · 1 across the 16 of their papers we have counts for
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Multiple Choice Learning for Efficient Speech Separation with Many Speakers
David Perera, François Derrida, Théo Mariotte +2
Training speech separation models in the supervised setting raises a permutation problem: finding the best assignation between the model predictions and the ground truth separated…
Using Random Codebooks for Audio Neural AutoEncoders
Benoît Giniès, Xiaoyu Bie, Olivier Fercoq +1
Latent representation learning has been an active field of study for decades in numerous applications. Inspired among others by the tokenization from Natural Language Processing an…
Learning Source Disentanglement in Neural Audio Codec
Xiaoyu Bie, Xubo Liu, Gaël Richard
Neural audio codecs have significantly advanced audio compression by efficiently converting continuous audio signals into discrete tokens. These codecs preserve high-quality sound…
Speech dereverberation constrained on room impulse response characteristics
Louis Bahrman, Mathieu Fontaine, Jonathan Le Roux +1
Single-channel speech dereverberation aims at extracting a dry speech signal from a recording affected by the acoustic reflections in a room. However, most current deep learning-ba…
Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing
David Perera, Victor Letzelter, Théo Mariotte +4
We introduce Annealed Multiple Choice Learning (aMCL) which combines simulated annealing with MCL. MCL is a learning framework handling ambiguous tasks by predicting a small set of…
Winner-takes-all learners are geometry-aware conditional density estimators
Victor Letzelter, David Perera, Cédric Rommel +4
Winner-takes-all training is a simple learning paradigm, which handles ambiguous tasks by predicting a set of plausible hypotheses. Recently, a connection was established between W…