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Stefan cel Mare
University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  4/2013 - 6

 HIGHLY CITED PAPER 

Microphone Clustering and BP Network based Acoustic Source Localization in Distributed Microphone Arrays

ZHANG, Q. See more information about ZHANG, Q. on SCOPUS See more information about ZHANG, Q. on IEEExplore See more information about ZHANG, Q. on Web of Science, CHEN, Z. See more information about  CHEN, Z. on SCOPUS See more information about  CHEN, Z. on SCOPUS See more information about CHEN, Z. on Web of Science, YIN, F. See more information about YIN, F. on SCOPUS See more information about YIN, F. on SCOPUS See more information about YIN, F. on Web of Science
 
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Download PDF pdficon (812 KB) | Citation | Downloads: 836 | Views: 4,281

Author keywords
acoustic source localization, BP neural network, microphone clustering, GCC-PHAT, TDOA

References keywords
processing(17), signal(14), source(11), speech(9), microphone(9), sound(8), localization(8), estimation(7), acoustics(7), network(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2013-11-30
Volume 13, Issue 4, Year 2013, On page(s): 33 - 40
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2013.04006
Web of Science Accession Number: 000331461300006
SCOPUS ID: 84890239023

Abstract
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A microphone clustering and back propagation (BP) neural network based acoustic source localization method using distributed microphone arrays in an intelligent meeting room is proposed. In the proposed method, a novel clustering algorithm is first used to divide all microphones into several clusters where each one corresponds to a specified BP network. Afterwards, the energy-based cluster selecting scheme is applied to select clusters which are small and close to the source. In each chosen cluster, the time difference of arrival of each microphone pair is estimated, and then all estimated time delays act as input of the corresponding BP network for position estimation. Finally, all estimated positions from the chosen clusters are fused for global position estimation. Only subsets rather than all the microphones are responsible for acoustic source localization, which leads to less computational cost; moreover, the local estimation in each selected cluster can be processed in parallel, which expects to improve the localization speed potentially. Simulation results from comparison with other related localization approaches confirm the validity of the proposed method.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 3,615 TCR
SCOPUS® Citations for all references: 4,796 TCR

Web of Science® Average Citations per reference: 129 ACR
SCOPUS® Average Citations per reference: 171 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-05-16 19:52 in 169 seconds.




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