Wavelet speech enhancement based on robust principal component analysis

Chia Lung Wu, Hsiang Ping Hsu, Syu Siang Wang, Jeih Weih Hung, Ying Hui Lai, Hsin Min Wang, Yu Tsao

研究成果: Conference article同行評審

1 引文 斯高帕斯(Scopus)

摘要

Most state-of-the-art speech enhancement (SE) techniques prefer to enhance utterances in the frequency domain rather than in the time domain. However, the overlap-add (OLA) operation in the short-time Fourier transform (STFT) for speech signal processing possibly distorts the signal and limits the performance of the SE techniques. In this study, a novel SE method that integrates the discrete wavelet packet transform (DWPT) and a novel subspace-based method, robust principal component analysis (RPCA), is proposed to enhance noise-corrupted signals directly in the time domain. We evaluate the proposed SE method on the Mandarin hearing in noise test (MHINT) sentences. The experimental results show that the new method reduces the signal distortions dramatically, thereby improving speech quality and intelligibility significantly. In addition, the newly proposed method outperforms the STFT-RPCA-based speech enhancement system.

原文English
頁(從 - 到)439-443
頁數5
期刊Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
2017-August
DOIs
出版狀態Published - 2017
事件18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 - Stockholm, Sweden
持續時間: 20 8月 201724 8月 2017

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