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Authordc.contributor.authorDehghan Firoozabadi, Ali 
Authordc.contributor.authorIrarrázaval, Pablo 
Authordc.contributor.authorAdasme, Pablo 
Authordc.contributor.authorZabala Blanco, David 
Authordc.contributor.authorPalacios Játiva, Pablo Geovanny 
Authordc.contributor.authorAzurdia Meza, César 
Cita de ítemdc.identifier.citationElectronics 2020, 9, 867es_ES
Abstractdc.description.abstractSound source localization is one of the applicable areas in speech signal processing. The main challenge appears when the aim is a simultaneous multiple sound source localization from overlapped speech signals with an unknown number of speakers. Therefore, a method able to estimate the number of speakers, along with the speaker's location, and with high accuracy is required in real-time conditions. The spatial aliasing is an undesirable effect of the use of microphone arrays, which decreases the accuracy of localization algorithms in noisy and reverberant conditions. In this article, a cuboids nested microphone array (CuNMA) is first proposed for eliminating the spatial aliasing. The CuNMA is designed to receive the speech signal of all speakers in different directions. In addition, the inter-microphone distance is adjusted for considering enough microphone pairs for each subarray, which prepares appropriate information for 3D sound source localization. Subsequently, a speech spectral estimation method is considered for evaluating the speech spectrum components. The suitable spectrum components are selected and the undesirable components are denied in the localization process. The speech information is different in frequency bands. Therefore, the adaptive wavelet transform is used for subband processing in the proposed algorithm. The generalized eigenvalue decomposition (GEVD) method is implemented in sub-bands on all nested microphone pairs, and the probability density function (PDF) is calculated for estimating the direction of arrival (DOA) in different sub-bands and continuing frames. The proper PDFs are selected by thresholding on the standard deviation (SD) of the estimated DOAs and the rest are eliminated. This process is repeated on time frames to extract the best DOAs. Finally, K-means clustering and silhouette criteria are considered for DOAs classification in order to estimate the number of clusters (speakers) and the related DOAs. All DOAs in each cluster are intersected for estimating the position of the 3D speakers. The closest point to all DOA planes is selected as a speaker position. The proposed method is compared with a hierarchical grid (HiGRID), perpendicular cross-spectra fusion (PCSF), time-frequency wise spatial spectrum clustering (TF-wise SSC), and spectral source model-deep neural network (SSM-DNN) algorithms based on the accuracy and computational complexity of real and simulated data in noisy and reverberant conditions. The results show the superiority of the proposed method in comparison with other previous works.es_ES
Patrocinadordc.description.sponsorshipComisión Nacional de Investigación Científica y Tecnológica (CONICYT) CONICYT FONDECYT 3190147 11180107 ANID PFCHA/Beca de Doctorado Nacional/2019 21190489es_ES
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile*
Link to Licensedc.rights.uri*
Keywordsdc.subjectSound source localizationes_ES
Keywordsdc.subjectNested microphone arrayes_ES
Keywordsdc.subjectSpectral estimationes_ES
Keywordsdc.subjectWavelet transformes_ES
Keywordsdc.subjectSubband processinges_ES
Títulodc.title3D multiple sound source localization by proposed cuboids nested microphone array in combination with adaptive Wavelet-based subband GEVDes_ES
Document typedc.typeArtículo de revistaes_ES
dcterms.accessRightsdcterms.accessRightsAcceso Abierto
Indexationuchile.indexArtículo de publicación ISI
Indexationuchile.indexArtículo de publicación SCOPUS

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Attribution-NonCommercial-NoDerivs 3.0 Chile
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile