A Study of Noise Suppression Approaches based on Nonnegative Matrix Factorization for Speech Enhancement

• Main Authors: Pao-han Lin, 林保翰
• Other Authors: Jeih-weih Hung
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/21870470709657212943

碩士 === 國立暨南國際大學 === 電機工程學系 === 102 === In this thesis, we exploit the technique of nonnegative matrix factorization (NMF) in speech enhancement, considering the sub-band and temporal patch characteristics of noisy spectrogram. We investigate the segmental NMF scheme in speech enhancement and compare it with the conventional frame-wise counterpart. Two forms of segmental NMF methods are investigated, which respectively decompose the spectrogram into temporal and spectral segmental parts, and then compensates each segment to reduce the noise effect. We evaluate these NMF-based methods in a subset of the Aurora-2 connected digit database. Experimental results show that these NMF-based methods can improve the quality of noise-corrupted speech signals, and they are well additive to two well-known methods, spectral subtraction (SS) and minimum mean-squared error (MMSE). We also show that the speech signals enhanced by NMF-based methods can result in better recognition accuracy relative to the original signals without enhancement.