On a dual-gain approach to noise reduction in the STFT domain

Chao Pan, Jingdong Chen, Jacob Benesty

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Noise reduction is typically achieved by applying a gain filter to the complex spectrum of the noisy speech signal in the short-time Fourier transform (STFT) domain. However, such an approach does not take into account the noncircularity property of the complex speech spectrum. Recently, a widely linear (WL) filtering framework was developed, which can fully take advantage of the second-order statistics of the noncircular speech spectrum for noise reduction. But the optimal WL filters are more complicated to estimate as the estimation involves the use of interframe information. In this paper, we investigate a dual-gain approach, which achieves noise reduction by applying one gain to filter the real part and another gain to filter the imaginary part of the complex noisy spectrum. We show that this approach can be viewed as a particular case of the WL framework. Compared to the classical method with a single gain, this new approach is shown to be able to achieve better noise reduction performance. Another advantage is that the optimal filters with this approach can be implemented using only the current frame of spectra without the need of the interframe information.

Original languageEnglish
Title of host publication2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2013
DOIs
StatePublished - 2013
Event2013 14th IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2013 - New Paltz, NY, United States
Duration: 20 Oct 201323 Oct 2013

Publication series

NameIEEE Workshop on Applications of Signal Processing to Audio and Acoustics

Conference

Conference2013 14th IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2013
Country/TerritoryUnited States
CityNew Paltz, NY
Period20/10/1323/10/13

Keywords

  • maximum SNR gains
  • Noise reduction
  • speech enhancement
  • STFT domain
  • tradeoff gains
  • Wiener gains

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