Manuel Pariente — accepted AI-conference papers

2023

Multi-Channel Speaker Extraction with Adversarial Training: The Wavlab Submission to The Clarity ICASSP 2023 Grand Challenge

Samuele Cornell, Zhong-Qiu Wang, Yoshiki Masuyama, Shinji Watanabe, Manuel Pariente, Nobutaka Ono +1

ICASSP 2023accepted

In this work we detail our submission to the Clarity ICASSP 2023 grand challenge, in which participants have to develop a strong target speech enhancement system for hearing-aid (HA) devices in noisy-reverberant environments. Our system builds on our previous submission at the Second Clarity Enhance…

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2022

Learning Filterbanks for End-to-End Acoustic Beamforming

Samuele Cornell, Manuel Pariente, François Grondin, Stefano Squartini

ICASSP 2022accepted

Recent work on monaural source separation has shown that performance can be increased by using fully learned filterbanks with short windows. On the other hand it is widely known that, for conventional beamforming techniques, performance increases with long analysis windows. This applies also to most…

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2020

Filterbank Design for End-to-end Speech Separation

Manuel Pariente, Samuele Cornell, Antoine Deleforge, Emmanuel Vincent

ICASSP 2020accepted

Single-channel speech separation has recently made great progress thanks to learned filterbanks as used in ConvTasNet. In parallel, parameterized filterbanks have been proposed for speaker recognition where only center frequencies and bandwidths are learned. In this work, we extend real-valued learn…

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