Speech Enhancement Based on the Integration of Fully Convolutional Network, Temporal Lowpass Filtering and Spectrogram Masking

• Main Authors: LIU,KUAN-YI, 劉冠毅
• Other Authors: HUNG,JEIH-WEHI
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/f6vwk9

碩士 === 國立暨南國際大學 === 電機工程學系 === 107 === In this study, we focus on the issue of noise distortion in speech signals, and develop two novel unsupervised speech enhancement algorithms including temporal lowpass filtering (TLF) and relative-to-maximum masking (RMM). Both of these two algorithms are conducted on the magnitude spectrogram of speech signals. TLF uses a simple moving-average filter to emphasize the low modulation frequencies of speech signals, which are believed to contain richer linguistic information and exhibit higher signal-to-noise ratios (SNR). Comparatively, in RMM we apply a mask that is directly multiplied with the speech spectrogram in a point-wise manner, and the used masking value is directly proportional to the magnitude of each temporal-frequency (T-F) point in the spectrogram. The preliminary experiments conducted on a subset of TIMIT database show that the two novel methods can promote the quality of noise-corrupted speech signals significantly, and both of them can be integrated with a well-known supervised speech enhancement scenario, namely fully convolutional network, to achieve even better perceptual speech quality values.