（1-2. School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641）
Abstract: To effectively extract inherent information from measured speech signals, it is important to preprocess data to reduce noise. In this letter, we propose an algorithm---Bayesian multi-solution shrinker (BMS) for speech enhancement. The basic idea of BMS is to utilize empirical Bayesian model in the wavelet coefficients shrinkage step. Using speech data and calculating signal-to-noise ratio (SNR) and segmental signal-to-noise ratio (SSNR), we show that shown that the proposed approach outperforms the benchmark methods based on log-spectral amplitude (LSA), spectral subtraction and Stein's Unbiased Risk Estimate (SURE) wavelet denoising, respectively.
Keywords: Speech enhancement; denoise; Bayesian multi-solution shrinker.
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作者簡介:
謝宗伯，博士，華南理工大學副研究員，IEEE/IEICE會員，主要研究方向為信號處理與機器學習。先后主持國家和省部級項目多項，在國際高水平期刊發表論文多篇。
馮久超，教授，博導，華南理工大學教授，IEEE會員，廣東省“珠江學者”特聘教授，主要研究方向為非線性系統理論。
