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Mathematical Problems in Engineering
Volume 2017, Article ID 1981280, 11 pages
Research Article

Compressing Sensing Based Source Localization for Controlled Acoustic Signals Using Distributed Microphone Arrays

1School of Physics and Technology, Nanjing Normal University, Nanjing 210097, China
2Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology, Nanjing 210044, China
3Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China

Correspondence should be addressed to Wei Ke; moc.anis@wkykw and Jianhua Shao; nc.ude.unjn@auhnaijoahs

Received 19 October 2016; Revised 9 January 2017; Accepted 19 February 2017; Published 8 August 2017

Academic Editor: Laurent Bako

Copyright © 2017 Wei Ke et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In order to enhance the accuracy of sound source localization in noisy and reverberant environments, this paper proposes an adaptive sound source localization method based on distributed microphone arrays. Since sound sources lie at a few points in the discrete spatial domain, our method can exploit this inherent sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing (CS) theory. In this method, a two-step discrete cosine transform- (DCT-) based feature extraction approach is utilized to cover both short-time and long-time properties of acoustic signals and reduce the dimensions of the sparse model. In addition, an online dictionary learning (DL) method is used to adjust the dictionary for matching the changes of audio signals, and then the sparse solution could better represent location estimations. Moreover, we propose an improved block-sparse reconstruction algorithm using approximate norm minimization to enhance reconstruction performance for sparse signals in low signal-noise ratio (SNR) conditions. The effectiveness of the proposed scheme is demonstrated by simulation results and experimental results where substantial improvement for localization performance can be obtained in the noisy and reverberant conditions.