A novel single channel speech enhancement approach by combining Wiener filter and dictionary learning

Hung Wei Tseng, Srikanth Vishnubhotla, Mingyi Hong, Jinjun Xiao, Zhi Quan Luo, Tao Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In this paper, a novel algorithm named Sparsity-based Wiener plus Dictionary Learning (SWDL) is proposed for single channel speech enhancement. SWDL combines both Wiener filter and dictionary learning technique. The Wiener filter is used to ensure the enhanced speech is statistically optimal, while the dictionary learning technique is used to improve the enhanced speech quality and intelligibility by utilizing speech-specific information. Such information is incorporated in the pre-trained speech dictionary that can sparsely represent the clean speech spectra. When applied to the TIM-IT database, SWDL outperforms the Log Mean Square-Error Short-Time Spectra Amplitude estimator (LSTSA) according to four different objective metrics measuring speech quality and intelligibility. Subjective tests also show that SWDL produces better speech quality and intelligibility than LSTSA.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages8653-8657
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • Dictionary Learning
  • Nonnegative Matrix Factorization
  • Speech Enhancement
  • Wiener Filtering

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