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

, , , , , , and . ICASSP, page 1-2. IEEE, (2023)

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Implicit and explicit phase modeling in deep learning-based source separation. (Modélisation implicite et explicite de la phase dans la séparation de sources par apprentissage profond).. University of Lorraine, Nancy, France, (2021)Asteroid: The PyTorch-Based Audio Source Separation Toolkit for Researchers., , , , , , , , , and 4 other author(s). INTERSPEECH, page 2637-2641. ISCA, (2020)Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge., , , , , , , , , and . Comput. Speech Lang., (2025)A Statistically Principled and Computationally Efficient Approach to Speech Enhancement Using Variational Autoencoders., , and . INTERSPEECH, page 3158-3162. ISCA, (2019)Multi-Channel Speaker Extraction with Adversarial Training: The Wavlab Submission to The Clarity ICASSP 2023 Grand Challenge., , , , , , and . ICASSP, page 1-2. IEEE, (2023)Multi-Channel Target Speaker Extraction with Refinement: The WavLab Submission to the Second Clarity Enhancement Challenge., , , , , and . CoRR, (2023)Task-Aware Separation for the DCASE 2020 Task 4 Sound Event Detection and Separation Challenge., , , , , , and . DCASE, page 31-35. (2020)Domain-Adversarial Training and Trainable Parallel Front-End for the DCASE 2020 Task 4 Sound Event Detection Challenge., , , , , , and . DCASE, page 26-30. (2020)Learning Filterbanks for End-to-End Acoustic Beamforming., , , and . ICASSP, page 6507-6511. IEEE, (2022)Filterbank Design for End-to-end Speech Separation., , , and . ICASSP, page 6364-6368. IEEE, (2020)