NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling

@inproceedings{Lee2022NASTARNA,
  title={NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling},
  author={Chi-Chang Lee and Cheng‐Hung Hu and Yu-Chen Lin and Chu-Song Chen and Hsin-Min Wang and Yu Tsao},
  booktitle={Interspeech},
  year={2022}
}
For deep learning-based speech enhancement (SE) systems, the training-test acoustic mismatch can cause notable performance degradation. To address the mismatch issue, numerous noise adap- tation strategies have been derived. In this paper, we propose a novel method, called noise adaptive speech enhancement with target-conditional resampling (NASTAR), which reduces mismatches with only one sample (one-shot) of noisy speech in the target environment. NASTAR uses a feedback mechanism to simulate… 
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