Three-dimensional source localization using sparse Bayesian learning on a spherical microphone array

Abstract

The identification of acoustic sources in a three-dimensional (3D) domain based on measurements with an array of microphones is a challenging problem: it entails the estimation of the angular position of the sources (direction of arrival), distance relative to the array (range), and the quantification of the source amplitudes. A 3D source localization model using a rigid spherical microphone array with spherical wave propagation is proposed. In this study, sparse Bayesian learning is used to perform localization in 3D space and examine the use of principal component analysis to denoise the measurement data. The performance of the proposed method is examined numerically and experimentally, which is tested both in a free-field and in a reverberant environment. The numerical and experimental investigations demonstrate that the approach offers accurate localization in a 3D domain, resolving closely spaced sources and making it possible to identify sources located at different ranges.

Frequency Analysis of Acoustic Data Using Multiple-Measurement Sparse Bayesian Learning

“…The SBL solves the linear system of Equation (4) while exploiting common frequency components across multiple measurements. As demonstrated in studies using the sparse representation [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ], the sparse estimations from the SBL using the multiple measurements have advantages for frequency detection in terms of enhancing resolution and reducing noise, which is supported by the comparison results of Section 4 and Section 5 . It is noteworthy that the feasibility of SBL in detecting low-frequency components in passive sonar signals is examined using the in-situ data by the current study.…”

“…Recently, the SBL has been used in finding the direction of arrivals (DOAs) [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ], localizing acoustic sources [ 35 , 36 , 37 , 38 ], and mode extraction [ 39 ]. Similar to CS, the SBL suffers from the basis mismatch arising from the discrete representation in the linear system, and the off-grid SBL models using approximations are proposed in order to relieve the problem [ 25 , 26 , 27 , 28 , 29 , 30 ].…”

“…Similar to CS, the SBL suffers from the basis mismatch arising from the discrete representation in the linear system, and the off-grid SBL models using approximations are proposed in order to relieve the problem [ 25 , 26 , 27 , 28 , 29 , 30 ]. On the other hand, noise in the measurements degrades the SBL based beamforming performance and the SBL is expanded to enhance the performance by using multiple measurements [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. In the SBL using multiple measurements, the commonality of the source signals over the multiple measurements is exploited, and it increases the robustness of the SBL to the noise.…”

“…Here, based on the properties demonstrated in the previous works [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ], the SBL using multiple measurements is used for detecting frequency components corresponding to the tonal signals in the passive sonar system after the linear system for the frequency analysis is established. The sparse tonal signals and the multiple measurements along the sensors in the sonar system should allow the SBL to have obvious frequency detections.…”

Anatomical Harmonics Basis based Brain Source Localization with Application to Epilepsy

“…Spherical harmonics domain processing has received significant attention for acoustic source localization and beamforming in the recent years due to ease of array processing in SH domain 31,32 . The head model considered herein is four shell spherical model.…”