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

Patrick Wijnings; Sander Stuijk; Bert de Vries; Henk Corporaal

doi:10.1109/ICASSP.2019.8682835

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

3 Citations (Scopus)

26 Downloads (Pure)

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 language	English
Title of host publication	2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	4325-4329
Number of pages	5
ISBN (Electronic)	978-1-4799-8131-1
DOIs	https://doi.org/10.1109/ICASSP.2019.8682835
Publication status	Published - May 2019
Event	2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019) - Brighton Conference Centre, Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019 https://2019.ieeeicassp.org/

Conference

Conference	2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019)
Abbreviated title	ICASSP 2019
Country/Territory	United Kingdom
City	Brighton
Period	12/05/19 → 17/05/19
Internet address	https://2019.ieeeicassp.org/

Keywords

Acoustic applications
Array signal processing
Robustness
Bayes methods

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10.1109/ICASSP.2019.8682835

Cite this

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). Article 8682835 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICASSP.2019.8682835

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title = "Robust Bayesian beamforming for sources at different distances with applications in urban monitoring",

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.",

keywords = "Acoustic applications, Array signal processing, Robustness, Bayes methods",

author = "Patrick Wijnings and Sander Stuijk and \{de Vries\}, Bert and Henk Corporaal",

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note = "2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019), ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",

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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., 8682835, Institute of Electrical and Electronics Engineers, Piscataway, pp. 4325-4329, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019), Brighton, United Kingdom, 12/05/19. https://doi.org/10.1109/ICASSP.2019.8682835

Robust Bayesian beamforming for sources at different distances with applications in urban monitoring. / Wijnings, Patrick; Stuijk, Sander ; de Vries, Bert et al.
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Piscataway: Institute of Electrical and Electronics Engineers, 2019. p. 4325-4329 8682835.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Robust Bayesian beamforming for sources at different distances with applications in urban monitoring

AU - Wijnings, Patrick

AU - Stuijk, Sander

AU - de Vries, Bert

AU - Corporaal, Henk

PY - 2019/5

Y1 - 2019/5

N2 - 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.

AB - 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.

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KW - Array signal processing

KW - Robustness

KW - Bayes methods

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BT - 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers

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T2 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019)

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Wijnings P, Stuijk S , de Vries B , Corporaal H. 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. Piscataway: Institute of Electrical and Electronics Engineers. 2019. p. 4325-4329. 8682835 doi: 10.1109/ICASSP.2019.8682835

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

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