Abstract
Multiple sound source localization is an important application in speech processing. In this paper, a cuboids nested microphone array (CuNMA) is proposed for sound acquisition. Also, the spatial aliasing is eliminated by the use of this array. Then, the subband processing is proposed based on the GammaTone filter bank. In the next, the generalized eigenvalue decomposition (GEVD) algorithm is implemented on all microphone pairs of CuNMA and for each obtained subband of the GammaTone filter bank. In each subband, the standard deviation (SD) is calculated for all direction of arrival (DOA) estimations, and the subbands with improper information are eliminated. Then, the K-means clustering with silhouette criteria are implemented on all DOAs for estimating the number of speakers and to allocate the related DOAs for each cluster. The proposed method is compared with steered response power-phase transform (SRP-PHAT), Geometric Projection, and spectral source model-deep neural network (SSM-DNN) on simulated data in noisy and reverberant conditions, which the results show the superiority of the proposed method in comparison with other previous works.
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The authors acknowledge financial support from: FONDECYT No. 3190147 and FONDECYT No. 11180107.
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Firoozabadi, A.D. et al. (2021). Simultaneous Sound Source Localization by Proposed Cuboids Nested Microphone Array Based on Subband Generalized Eigenvalue Decomposition. In: Hassanien, A.E., Slowik, A., Snášel, V., El-Deeb, H., Tolba, F.M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing, vol 1261. Springer, Cham. https://doi.org/10.1007/978-3-030-58669-0_72
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