Solution strategies for linear inverse problems in spatial audio signal processing

Mingsian R. Bai*, Chun Chung, Po Chen Wu, Yi Hao Chiang, Chun-May Yang


研究成果: Article同行評審

11 引文 斯高帕斯(Scopus)


The aim of this study was to compare algorithms for solving inverse problems generally encountered in spatial audio signal processing. Tikhonov regularization is typically utilized to solve overdetermined linear systems in which the regularization parameter is selected by the golden section search (GSS) algorithm. For underdetermined problems with sparse solutions, several iterative compressive sampling (CS) methods are suggested as alternatives to traditional convex optimization (CVX) methods that are computationally expensive. The focal underdetermined system solver (FOCUSS), the steepest descent (SD) method, Newton's (NT) method, and the conjugate gradient (CG) method were developed to solve CS problems more efficiently in this study. These algorithms were compared in terms of problems, including source localization and separation, noise source identification, and analysis and synthesis of sound fields, by using a uniform linear array (ULA), a uniform circular array (UCA), and a random array. The derived results are discussed herein and guidelines for the application of these algorithms are summarized.

原文American English
期刊Applied Sciences (Switzerland)
出版狀態Published - 5 6月 2017


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