Dereverberation based on bin-wise temporal variations of complex spectrogram

Tzu Hao Chen, Chun Huang, Tai-Shih Chi

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

Abstract

Humans analyze sounds not only based on their frequency contents, but also on the temporal variations of the frequency contents. Inspired by auditory perception, we propose a deep neural network (DNN) based dereverberation algorithm in the rate domain, which presents the temporal variations of frequency contents, in this paper. We show convolutional noise in the time domain can be approximated to multiplicative noise in the rate domain. To remove the multiplicative noise, we adopt the rate-domain complex-valued ideal ratio mask (RDcIRM) as the training target of the DNN. Simulation results show that the proposed rate-domain DNN algorithm is more capable of recovering high-intelligible and high-quality speech from reverberant speech than the compared state-of-the-art dereverberation algorithm. Hence, it is highly suitable for speech applications involving human listeners.

Original languageEnglish
Title of host publication2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5635-5639
Number of pages5
ISBN (Electronic)9781509041176
DOIs
StatePublished - 16 Jun 2017
Event2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States
Duration: 5 Mar 20179 Mar 2017

Publication series

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

Conference

Conference2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017
CountryUnited States
CityNew Orleans
Period5/03/179/03/17

Keywords

  • deep neural network
  • Dereverberation
  • ideal ratio mask
  • modulation spectrum

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