Study of widely linear multichannel wiener filter for binaural noise reduction

Xin Leng, Jingdong Chen, Israel Cohen, Jacob Benesty

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

Abstract

In this paper, we study the binaural noise-reduction problem using an array of microphones. The widely linear (WL) framework in the short-time-Fourier-transform (STFT) domain is adopted. In such a framework, the microphone array signals and binaural outputs are first merged into complex signals. These complex signals are subsequently transformed into the STFT domain. The WL estimation theory is then applied in STFT subbands with interband correlation to form the optimal WL Wiener filter, which exploits the noncircular properties of the input complex signals to achieve noise reduction and meanwhile to preserve the sound spatial realism. Finally, the time-domain binaural output is reconstructed from the output of the WL Wiener filter using the inverse STFT. The effectiveness of the developed STFT-domain WL Wiener filter for binaural noise reduction is justified using experiments.

Original languageEnglish
Title of host publication25th European Signal Processing Conference, EUSIPCO 2017
Pages21-25
Number of pages5
ISBN (Electronic)9780992862671
DOIs
StatePublished - 23 Oct 2017
Event25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece
Duration: 28 Aug 20172 Sep 2017

Publication series

Name25th European Signal Processing Conference, EUSIPCO 2017
Volume2017-January

Conference

Conference25th European Signal Processing Conference, EUSIPCO 2017
Country/TerritoryGreece
CityKos
Period28/08/172/09/17

All Science Journal Classification (ASJC) codes

  • Signal Processing

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