Robust Bayesian and Maximum a Posteriori Beamforming for Hearing Assistive Devices

Poul Hoang, Zheng-Hua Tan, Jan Mark de Haan, Thomas Lunner, Jesper Jensen

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

1 Citation (Scopus)

Abstract

Multi-microphone speech enhancement systems often apply beamforming to enhance one or multiple desired signals in a noisy environment. Common for many beamforming methods, is that they require the direction-of-arrival (DOA) of the target sound source to be known in order to achieve optimal noise reduction performance. To improve robustness against DOA uncertainty, we propose maximum a posteriori (MAP) and Bayesian beamformers that are able to take advantage of prior information on the target direction. We compare the proposed MAP and Bayesian beamformers to state-of-the-art beamforming methods for noise reduction in hearing assistive devices. We evaluate the proposed beamformers in isotropic babble noise in terms of segmental SNR (SSNR) and extended short-time objective intelligibility (ESTOI). Results show that the proposed methods outperform current state-of-the-art beamformers used for noise reduction in hearing aids in most scenarios.
Original languageEnglish
Title of host publicationIEEE Global Conference on Signal and Information Processing (GlobalSIP)
Number of pages5
PublisherIEEE
Publication date27 Jan 2020
Article number8969234
ISBN (Print)978-1-7281-2724-8
ISBN (Electronic)978-1-7281-2723-1
DOIs
Publication statusPublished - 27 Jan 2020
Event2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) - Ottawa, Canada
Duration: 11 Nov 201914 Nov 2019

Conference

Conference2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP)
CountryCanada
CityOttawa
Period11/11/201914/11/2019
SeriesIEEE Global Conference on Signal and Information Processing (GlobalSIP). Proceedings

Keywords

  • Bayesian beamforming
  • Hearing aids
  • Maximum a posteriori beamforming
  • Noise reduction
  • Spatial filtering

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