Paper Info
Title
DNN-Based Low-Musical-Noise Single-Channel Speech Enhancement Based on Higher-Order-Moments Matching
Abstract
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 affects 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 effectively the higher-order moments reduce the amount of musical noise. Experimental evaluation results 1) demonstrate that kurtosis matching can reduce musical noise without negatively affecting noise suppression and 2) newly reveal that the sixth-moment matching also achieves low-musical-noise speech enhancement as well as kurtosis matching.
Year
DOI
Venue
2021
10.1587/transinf.2021EDP7041
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
DocType
Volume
speech enhancement, musical noise, kurtosis, moment matching, deep learning
Journal
E104D
Issue
ISSN
Citations 
11
1745-1361
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Satoshi Mizoguchi100.34
Yuki Saito200.68
Shinnosuke Takamichi300.68
Saruwatari, H.465290.81