Glottal model based speech beamforming for Ad-hoc microphone arrays

Yang Zhang, Dinei Florèncio, Mark Hasegawa-Johnson

Research output: Contribution to journalConference articlepeer-review


We are interested in the task of speech beamforming in conference room meetings, with microphones built in the electronic devices brought and casually placed by meeting participants. This task is challenging because of the inaccuracy in position and interference calibration due to random microphone configuration, variance of microphone quality, reverberation etc. As a result, not many beamforming algorithms perform better than simply picking the closest microphone in this setting. We propose a beamforming called Glottal Residual Assisted Beamforming (GRAB). It does not rely on any position or interference calibration. Instead, it incorporates a source-filter speech model and minimizes the energy that cannot be accounted for by the model. Objective and subjective evaluations on both simulation and real-world data show that GRAB is able to suppress noise effectively while keeping the speech natural and dry. Further analyses reveal that GRAB can distinguish contaminated or reverberant channels and take appropriate action accordingly.

Original languageEnglish (US)
Pages (from-to)2675-2679
Number of pages5
JournalProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
StatePublished - 2017
Event18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 - Stockholm, Sweden
Duration: Aug 20 2017Aug 24 2017


  • Ad-Hoc Microphone Array
  • Beamforming
  • Lpc Residual
  • Speech Enhancement
  • Speech Model

ASJC Scopus subject areas

  • Language and Linguistics
  • Human-Computer Interaction
  • Signal Processing
  • Software
  • Modeling and Simulation

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