Indoor Sound Source Localization based on Sparse Bayesian Learning and Compressed Data

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Abstrakt

In this paper, the problems of indoor sound source
localization using a wireless acoustic sensor network are addressed and a new sparse Bayesian learning based algorithm is
proposed. Using time delays for the direct paths from candidate source locations to microphone nodes, the proposed algorithm estimates the most likely source location. To reduce the amount of data that must be exchanged between microphone nodes, a Gaussian measurement matrix is multiplied on to each channel and the proposed method operates directly on the compressed data. This is achieved by exploiting sparsity in both the frequency and space domains. The performance is analysed in numerical simulations, where the performance as a function of the reverberation times in investigated, and the results show that the proposed algorithm is robust to reverberation.
OriginalsprogEngelsk
Titel2019 27th European Signal Processing Conference (EUSIPCO)
ForlagIEEE
Publikationsdato2019
Artikelnummer8903069
ISBN (Trykt)978-90-827970-2-2 (USB)
ISBN (Elektronisk)978-9-0827-9703-9
DOI
StatusUdgivet - 2019
Begivenhed27th European Signal Processing Conference, EUSIPCO 2019 - Coruña, Spanien
Varighed: 2 sep. 20196 sep. 2019

Konference

Konference27th European Signal Processing Conference, EUSIPCO 2019
LandSpanien
ByCoruña
Periode02/09/201906/09/2019
NavnProceedings of the European Signal Processing Conference
ISSN2076-1465

Emneord

  • sound source localization
  • sparse Bayesian learning
  • array signal processing
  • reverberation

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