DocumentCode
88365
Title
A Family of Maximum SNR Filters for Noise Reduction
Author
Gongping Huang ; Benesty, Jacob ; Tao Long ; Jingdong Chen
Author_Institution
Center of Immersive & Intell. Acoust., Northwestern Polytech. Univ., Xian, China
Volume
22
Issue
12
fYear
2014
fDate
Dec. 2014
Firstpage
2034
Lastpage
2047
Abstract
This paper is devoted to the study and analysis of the maximum signal-to-noise ratio (SNR) filters for noise reduction both in the time and short-time Fourier transform (STFT) domains with one single microphone and multiple microphones. In the time domain, we show that the maximum SNR filters can significantly increase the SNR but at the expense of tremendous speech distortion. As a consequence, the speech quality improvement, measured by the perceptual evaluation of speech quality (PESQ) algorithm, is marginal if any, regardless of the number of microphones used. In the STFT domain, the maximum SNR filters are formulated by considering the interframe information in every frequency band. It is found that these filters not only improve the SNR, but also improve the speech quality significantly. As the number of input channels increases so is the gain in SNR as well as the speech quality. This demonstrates that the maximum SNR filters, particularly the multichannel ones, in the STFT domain may be of great practical value.
Keywords
Fourier transforms; filtering theory; microphones; signal denoising; PESQ algorithm; STFT domain; interframe information; maximum SNR filter; microphone; noise reduction; perceptual evaluation of speech quality algorithm; short-time Fourier transform domain; signal-to-noise ratio filter; speech distortion; speech quality improvement; Correlation; Microphones; Noise reduction; Signal to noise ratio; Speech; Vectors; Maximum SNR filter; multichannel; noise reduction; short-time Fourier transform (STFT) domain; single channel; speech enhancement; time domain;
fLanguage
English
Journal_Title
Audio, Speech, and Language Processing, IEEE/ACM Transactions on
Publisher
ieee
ISSN
2329-9290
Type
jour
DOI
10.1109/TASLP.2014.2360643
Filename
6911982
Link To Document