Robust speech dereverberation with a neural network-based post-filter that exploits multi-conditional training of binaural cues

Tobias May*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

553 Downloads (Pure)

Abstract

This study presents an algorithm for binaural speech dereverberation based on the supervised learning of short-term binaural cues. The proposed system combined a delay-and-sum beamformer (DSB) with a neural network-based post-filter that attenuated reverberant components in individual time-frequency (T-F) units. A multi-conditional training (MCT) procedure was used to simulate the uncertainties of short-term binaural cues in response to room reverberation by mixing the direct part of head related impulse responses (HRIRs) with diffuse noise. Despite being trained with only anechoic HRIRs, the proposed dereverberation algorithm was tested in a variety of reverberant environments and achieved considerable improvements relative to a coherence-based approach in terms of three objective metrics reflecting speech quality and speech intelligibility. Moreover, a systematic evaluation showed that the proposed system generalized very well to a wide range of acoustic conditions, including various measured binaural room impulse responses (BRIRs) reflecting different reverberation times, azimuth positions spanning the entire frontal hemifield, various source-receiver distances as well as different artificial heads.
Original languageEnglish
JournalIEEE/ACM Transactions on Audio, Speech, and Language Processing
Volume26
Issue number2
Pages (from-to)406-414
ISSN2329-9290
DOIs
Publication statusPublished - 2018

Bibliographical note

Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission.

Keywords

  • Binaural
  • Coherence
  • Dereverberation
  • Ideal ratio mask
  • Neural networks
  • Short-term direct-to-reverberant energy ratio

Fingerprint Dive into the research topics of 'Robust speech dereverberation with a neural network-based post-filter that exploits multi-conditional training of binaural cues'. Together they form a unique fingerprint.

Cite this