Patch-based domain adversarial training for speech enhancement
Year: 2024
Type: article
Abstract: In deep learning-based speech enhancement methods, mismatched distributions between training data and test data are often encountered. These mismatches can include differences in speakers, speech content, noise types, and signal-to-noise ratios between the datasets. Severe data mismatches can significantly degrade the performance of speech enhancement. To address this issue, a speech enhancement method based on Patch domain adversarial training was proposed. Building on previous domain adversarial training methods for speech enhancement, implicit modeling of a domain discriminator was employed, allowing the entire speech signal to be divided into multiple independent patches for discrimination. Adaptive learning of the training data was enabled, thereby reducing distribution differences between the training and test data and improving the model’s enhancement capabilities on test data. Experimental results show that this method exhibits superior performance compared to previous methods under various degrees of data mismatch and maintains good stability as an adversarial training approach.
Authors: WANG Hongtao, LU Zhihua, YE Qingwei, ZHANG Lianjun
Language: English
Topic: Speech and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7014397536
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