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Acoustic Source Localization and Deconvolution-Based Separation
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DOI：10.1142/s0218396x15500083.png)
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Abstract
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This paper examines two fundamental issues in sound field analysis: acoustic sources localization and separation. Algorithms are developed to locate and separate acoustic signals on the basis of plane-wave decomposition. In the localization stage, directions of plane waves are determined using either minimum variance distortionless response (MVDR) method or multiple signal classification (MUSIC) method. For broadband scenarios, coherent and incoherent techniques are utilized in the localization procedure. In the separation stage, two approaches with overdetermined and underdetermined settings can be employed. In the overdetermined approach, Tikhonov regularization (TIKR) is utilized to recover the source signals. In the underdetermined approach, the steering matrix is augmented by including the directions that have been determined in the localization stage. Hence, the separation problem is formulated into a compressive sensing (CS) problem which can be effectively solved by using convex (CVX) optimization. Simulation and experiments are conducted for a 24-element circular array. Objective tests using perceptual evaluation of speech quality (PESQ) tests and subjective listening tests demonstrate that the proposed methods yield speech signals with well separated and improved quality, as compared to the mixed signals.
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