DNN-Based Low-Musical-Noise Single-Channel Speech Enhancement Based on Higher-Order-Moments Matching
DNN-Based Low-Musical-Noise Single-Channel Speech Enhancement Based on Higher-Order-Moments Matching
复制标题
DOI:
10.1587/transinf.2021edp7041
复制
发表时间:
2021-11
期刊:
影响因子:
--
通讯作者:
Satoshi Mizoguchi;Yuki Saito;Shinnosuke Takamichi;H. Saruwatari
中科院分区:
文献类型:
--
作者:
Satoshi Mizoguchi;Yuki Saito;Shinnosuke Takamichi;H. Saruwatari
SUMMARY We propose deep neural network (DNN)-based speech enhancement that reduces musical noise and achieves better auditory impressions. The musical noise is an artifact generated by nonlinear signal processing and negatively a ff ects the auditory impressions. We aim to develop musical-noise-free speech enhancement methods that suppress the musical noise generation and produce perceptually-comfortable enhanced speech. DNN-based speech enhancement using a soft mask achieves high noise reduction but generates musical noise in non-speech regions. Therefore, first, we define kurtosis matching for DNN-based low-musical-noise speech enhancement. Kurtosis is the fourth-order moment and is known to correlate with the amount of musical noise. The kurtosis matching is a penalty term of the DNN training and works to reduce the amount of musical noise. We further extend this scheme to standardized-moment matching. The extended scheme involves using moments whose orders are higher than kurtosis and generalizes the conventional musical-noise-free method based on kurtosis matching. We formulate standardized-moment matching and explore how e ff ectively the higher-order moments reduce the amount of musical noise. Experimental evaluation results 1) demonstrate that kurtosis matching can reduce musical noise without negatively a ff ecting noise suppression and 2) newly reveal that the sixth-moment matching also achieves low-musical-noise speech enhancement as well as kurtosis matching.