Continual Self-Training With Bootstrapped Remixing For Speech Enhancement
Author:
Affiliation:
1. University of Illinois at Urbana-Champaign
2. Meta AI Research
3. Meta Reality Labs Research
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/9745891/9746004/09747463.pdf?arnumber=9747463
Reference29 articles.
1. Teacher-student Deep Clustering for Low-delay Single Channel Speech Separation
2. Teacher-Student MixIT for Unsupervised and Semi-Supervised Speech Separation
3. Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation
4. Semi-Supervised Singing Voice Separation With Noisy Self-Training
5. Momentum Pseudo-Labeling for Semi-Supervised Speech Recognition
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1. Objective and subjective evaluation of speech enhancement methods in the UDASE task of the 7th CHiME challenge;Computer Speech & Language;2025-01
2. A review of deep learning techniques for speech processing;Information Fusion;2023-11
3. Self-Remixing: Unsupervised Speech Separation VIA Separation and Remixing;ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2023-06-04
4. Music Source Separation With Band-Split RNN;IEEE/ACM Transactions on Audio, Speech, and Language Processing;2023
5. MixCycle: Unsupervised Speech Separation via Cyclic Mixture Permutation Invariant Training;IEEE Signal Processing Letters;2022
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