Speech enhancement based on the integration of fully convolutional network, temporal lowpass filtering and spectrogram masking

Speech enhancement based on the integration of fully convolutional network, temporal lowpass filtering and spectrogram masking

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 (TLP) and relative-to-maximum masking (RMM). Both of these two algorithms are conducted on the magnitude spectrogram of speech signals. TLP 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 pointwise 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 noisecorrupted 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.