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Keywords = A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication

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12 pages, 325 KB  
Article
A Supervised Speech Enhancement Approach with Residual Noise Control for Voice Communication
by Andong Li, Renhua Peng, Chengshi Zheng and Xiaodong Li
Appl. Sci. 2020, 10(8), 2894; https://doi.org/10.3390/app10082894 - 22 Apr 2020
Cited by 9 | Viewed by 5996
Abstract
For voice communication, it is important to extract the speech from its noisy version without introducing unnaturally artificial noise. By studying the subband mean-squared error (MSE) of the speech for unsupervised speech enhancement approaches and revealing its relationship with the existing loss function [...] Read more.
For voice communication, it is important to extract the speech from its noisy version without introducing unnaturally artificial noise. By studying the subband mean-squared error (MSE) of the speech for unsupervised speech enhancement approaches and revealing its relationship with the existing loss function for supervised approaches, this paper derives a generalized loss function that takes residual noise control into account with a supervised approach. Our generalized loss function contains the well-known MSE loss function and many other often-used loss functions as special cases. Compared with traditional loss functions, our generalized loss function is more flexible to make a good trade-off between speech distortion and noise reduction. This is because a group of well-studied noise shaping schemes can be introduced to control residual noise for practical applications. Objective and subjective test results verify the importance of residual noise control for the supervised speech enhancement approach. Full article
(This article belongs to the Collection Recent Applications of Active and Passive Noise Control)
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