Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81851
Title: Swarm Intelligence Based Particle Filter for Alternating Talker Localization and Tracking Using Microphone Arrays
Authors: Goh, Shu Ting
Wu, Kai
Reju, Vaninirappuputhenpurayil Gopalan
Khong, Andy Wai Hoong
Keywords: Talker localization and tracking
Microphone arrays
Issue Date: 2016
Source: Wu, K., Reju, V. G., Khong, A. W. H., & Goh, S. T. (2017). Swarm Intelligence Based Particle Filter for Alternating Talker Localization and Tracking Using Microphone Arrays. IEEE/ACM Transactions on Audio, Speech and Language Processing, 25(6), 1384-1397.
Series/Report no.: IEEE/ACM Transactions on Audio, Speech and Language Processing
Abstract: We address the problem of localizing and tracking alternating (moving or stationary) talkers using microphone arrays in a room environment. One of the main challenges is the frequent (and possibly abrupt) change of talker positions which requires the algorithm to capture the active talker rapidly. In addition, the presence of interference, background noise and room reverberation degrades the tracking performance. We propose a new algorithm that jointly exploits the advantages of the particle filter (PF) and particle swarm intelligence. The PF is used as a general tracking framework which incorporates a proposed alternating source-dynamic model for recursive estimation of talker position. Unlike the conventional PF where particles operate independently in the particle sampling stage, the use of swarm intelligence allows particles to interact with each other, thereby improving convergence toward the active talker location. In addition, the memory mechanism in swarm intelligence allows particles to remain at their previous best-fit state estimate when signals are corrupted by interference, noise and/or reverberation. Simulations and experiments were conducted to demonstrate the effectiveness of the proposed algorithm.
URI: https://hdl.handle.net/10356/81851
http://hdl.handle.net/10220/42286
ISSN: 2329-9290
DOI: 10.1109/TASLP.2017.2693566
Rights: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [https://doi.org/10.1109/TASLP.2017.2693566].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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