https://scholars.lib.ntu.edu.tw/handle/123456789/633099
標題: | NASTAR: Noise Adaptive Speech Enhancement with Target-Conditional Resampling | 作者: | Lee, Chi Chang Hu, Cheng Hung YU-CHEN LIN CHU-SONG CHEN Wang, Hsin Min Tsao, Yu |
關鍵字: | acoustic retrieval | contrastive learning | noise adaptation | source separation | speech enhancement | 公開日期: | 1-一月-2022 | 卷: | 2022-September | 來源出版物: | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH | 摘要: | 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 adaptation 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 adaptive training data via a noise extractor and a retrieval model. The noise extractor estimates the target noise from the noisy speech, called pseudo-noise. The noise retrieval model retrieves relevant noise samples from a pool of noise signals according to the noisy speech, called relevant-cohort. The pseudo-noise and the relevant-cohort set are jointly sampled and mixed with the source speech corpus to prepare simulated training data for noise adaptation. Experimental results show that NASTAR can effectively use one noisy speech sample to adapt an SE model to a target condition. Moreover, both the noise extractor and the noise retrieval model contribute to model adaptation. To our best knowledge, NASTAR is the first work to perform one-shot noise adaptation through noise extraction and retrieval. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/633099 | ISSN: | 2308457X | DOI: | 10.21437/Interspeech.2022-527 |
顯示於: | 資訊工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。