Evaluation of localization precision by proposed quasi-spherical nested microphone array in combination with multiresolution adaptive steered response power
Author
dc.contributor.author
Firoozabadi, Ali Dehghan
Author
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Irarrázaval, Pablo
Author
dc.contributor.author
Adasme, Pablo
Author
dc.contributor.author
Zabala Blanco, David
Author
dc.contributor.author
Azurdia Meza, César
Admission date
dc.date.accessioned
2020-11-05T18:31:31Z
Available date
dc.date.available
2020-11-05T18:31:31Z
Publication date
dc.date.issued
2020
Cita de ítem
dc.identifier.citation
Journal of Electrical Engineering, Vol. 71 (2020), No. 3, 150–164
es_ES
Identifier
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10.2478/jee-2020-0022
Identifier
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https://repositorio.uchile.cl/handle/2250/177574
Abstract
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Multiple sound source localization in noisy and reverberant conditions is one of the important challenges in the speech signal processing. The aim of this article is three-dimensional sound source localization in undesirable scenarios. For the localization algorithms, the spatial aliasing is one of the destructive factors in reducing the accuracy. Firstly, a 3D quasi-spherical nested microphone array (QSNMA) is proposed for eliminating the spatial aliasing. Since the speech signal has the windowed-disjoint orthogonality property, the speech information differs in terms of the frequency bands. Then, the Gammatone filter bank is introduced for the speech subband processing. In the following, the multiresolution steered response power (SRP) algorithm is adaptively implemented on subbands with the phase transform (PHAT)/maximum likelihood (ML) weighted functions based on the levels of the noise and reverberation. The peaks of the multiresolution adaptive SRP (MASRP) algorithm are extracted in each subband based on the number of speakers for continuous time frames. Finally, the distribution of these peaks are calculated in each subband and they are merged by the use of weighted averaging method. The final 3D speakers locations are estimated by extracting the peaks in the final distribution. The proposed QSNMA-MASRP(PHAT/ML) algorithm is evaluated on real and simulated data for 2 and 3 simultaneous speakers in noisy and reverberant conditions. The proposed method is compared with SRP-PHAT, spectral source model-deep neural network, and spherical harmonic temporal extension of multiple response model sparse Bayesian learning algorithms on different range of signal-to-noise ratio and reverberation time. The mean absolute estimation error, averaged standard deviation for absolute estimation error, and computational complexity results show the superiority of the proposed method.
es_ES
Patrocinador
dc.description.sponsorship
Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)
CONICYT FONDECYT
3190147
Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT)
CONICYT FONDECYT
11180107
Evaluation of localization precision by proposed quasi-spherical nested microphone array in combination with multiresolution adaptive steered response power