Robust Bayesian beamforming for sources at different distances with applications in urban monitoring

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Abstract

Acoustic smart sensor networks can provide valuable actionable intelligence to authorities for managing safety in the urban environment. A spatial filter (beamformer) for localization and separation of acoustic sources is a key component of such a network. However, classical methods such as delay-and-sum beamforming fail, because sources are located at varying distances from the sensor array. This causes a regularization problem where either far-away sources are wrongly attenuated, or noise is wrongly amplified.

We solve this by considering source strength and location as random variables. The posterior distributions are approximated using Gibbs sampling. Each marginal is computed by combining importance sampling and inverse transform sampling using Chebyshev polynomial approximation. This leads to an iterative algorithm with similarities to deconvolution beamforming.

Our method is robust against deviations in manifold model, can deal with sources at different distances and power levels, and does not require an a priori known number of sources.
Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages4325-4329
Number of pages5
ISBN (Electronic)978-1-4799-8131-1
DOIs
Publication statusPublished - May 2019
Event2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Brighton Conference Centre, Brighton, United Kingdom
Duration: 12 May 201917 May 2019
Conference number: 2019
https://2019.ieeeicassp.org/

Conference

Conference2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Abbreviated titleICASSP
CountryUnited Kingdom
CityBrighton
Period12/05/1917/05/19
Internet address

Keywords

  • Acoustic applications
  • Array signal processing
  • Robustness
  • Bayes methods

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    Wijnings, P., Stuijk, S., de Vries, B., & Corporaal, H. (2019). Robust Bayesian beamforming for sources at different distances with applications in urban monitoring. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 4325-4329). [8682835] Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSP.2019.8682835