Hearing aid-controlled beamformer for binaural speech enhancement using a model-based approach

Mathew Shaji Kavalekalam, Jesper Kjær Nielsen, Mads Græsbøll Christensen, Jesper Boldt

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

3 Citations (Scopus)
304 Downloads (Pure)

Abstract

The understanding of speech from a particular speaker in the presence of other interfering speakers can be severely degraded for a hearing impaired person. Beamforming techniques have been proven to be effective to improve the speech understanding in such scenarios. However, the number of microphones in a hearing aid (HA) is limited due to the space and power constraints present in the HA. In this paper, we propose to use an external device e.g., a microphone array, that can communicate with the HA to overcome this limitation. We propose a method to control this external device based on the look direction of the HA user. We show, by means of simulations, the robustness of the proposed method at very low SNRs in a reverberant scenario. Moreover, we have also conducted experiments that show the benefit of using this framework for binaural and monaural enhancement.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages5
PublisherIEEE
Publication dateMay 2019
Pages321-325
ISBN (Electronic)978-1-4799-8131-1
DOIs
Publication statusPublished - May 2019
EventICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Brighton, United Kingdom,
Duration: 12 May 201917 May 2019
https://ieeexplore.ieee.org/xpl/conhome/8671773/proceeding

Conference

ConferenceICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
LocationBrighton, United Kingdom,
Period12/05/201917/05/2019
Internet address
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Keywords

  • Speech enhancement
  • autoregressive models
  • beamforming

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