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A NON-PARAMETRIC BAYESIAN MODEL FOR SUPPRESSING THE INTERFERENCE IN THE ACOUSTIC ARRAY MEASUREMENT

  • Liang Yu
  • , Yongli Zhang
  • , Mingsheng Lyu
  • , Ran Wang
  • , Yong Fang
  • , Weikang Jiang
  • Shanghai Jiao Tong University
  • Shanghai Maritime University
  • Shanghai University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Microphone array technology has a wide range of applications in areas such as mechanical noise source identification and aircraft noise source identification. The acoustic array is not only applied in anechoic chamber measurements, but also in in-situ measurements, which is an essential requirement for current applications. However, the microphone array measurements are inevitably affected by background interference when we wish to implement in-situ measurements. This paper proposes a general array denoising algorithm to address this issue. Considering the complexity of background interference, a Gaussian mixture model that can fit any probability distribution is constructed. The background interference tends to have non-independently and non-identically distributed characteristics between different microphone channels. The hierarchical Dirichlet process is applied to the Gaussian mixture model to avoid selecting the Gaussian component number. At the same time, a low-rank model of the sound source signal is constructed according to its correlation characteristics between microphones. All involved parameters of the proposed model are solved by the variational Bayesian inference. The sound source signal and the complex background interference are eventually separated. The performance of the proposed algorithm is evaluated in numerical simulation and laboratory experiments. Both the effectiveness and robustness of the proposed algorithm in suppressing the complex background interference are also verified.

Original languageEnglish
Title of host publicationProceedings of the 29th International Congress on Sound and Vibration, ICSV 2023
EditorsEleonora Carletti
PublisherSociety of Acoustics
ISBN (Electronic)9788011034238
StatePublished - 2023
Externally publishedYes
Event29th International Congress on Sound and Vibration, ICSV 2023 - Prague, Czech Republic
Duration: 9 Jul 202313 Jul 2023

Publication series

NameProceedings of the International Congress on Sound and Vibration
ISSN (Electronic)2329-3675

Conference

Conference29th International Congress on Sound and Vibration, ICSV 2023
Country/TerritoryCzech Republic
CityPrague
Period9/07/2313/07/23

Keywords

  • Acoustic array measurement
  • Acoustic imaging with strong interference
  • Noise measurement
  • Nonparametric Bayesian model
  • Variational Bayesian Inference

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