Continual Self-Training With Bootstrapped Remixing For Speech Enhancement

@article{Tzinis2022ContinualSW,
  title={Continual Self-Training With Bootstrapped Remixing For Speech Enhancement},
  author={Efthymios Tzinis and Yossi Adi and Vamsi Krishna Ithapu and Buye Xu and Anurag Kumar},
  journal={ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2022},
  pages={6947-6951}
}
  • Efthymios TzinisYossi Adi Anurag Kumar
  • Published 19 October 2021
  • Computer Science
  • ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
We propose RemixIT, a simple and novel self-supervised training method for speech enhancement. The proposed method is based on a continuously self-training scheme that overcomes limitations from previous studies including assumptions for the in-domain noise distribution and having access to clean target signals. Specifically, a separation teacher model is pre-trained on an out-of-domain dataset and is used to infer estimated target signals for a batch of in-domain mixtures. Next, we bootstrap… 
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