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• W2921138327 abstract "Speaker tracking in distributed microphone networks is a challenging task due to the adverse effects of reverberation and noise. In this paper, a speaker tracking method based on distributed particle filter (DPF) and modified iterative covariance intersection (MICI) algorithm is proposed in distributed microphone networks. Specifically, the time difference of arrival (TDOA) of speech signals received by a pair of microphones at each node is first estimated using the generalized cross-correlation function. Next, multiple TDOAs are considered as the local measurement via a choice strategy and the multiple-hypothesis model is used as the local likelihood function of the DPF. Finally, the MICI algorithm is proposed to fuse local estimates to implement a global consistent speaker tracking where the local estimates at individual nodes are allowed to be unknown cross-correlations. The proposed method can successfully track the speaker in reverberant and noisy environments, and it is robust to the node faults and suitable to track speaker in networks with the unknown number of nodes. Simulation results demonstrate that the proposed method has better performance over the existing methods when SNR <inline-formula xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink><tex-math notation=LaTeX>$< $</tex-math></inline-formula> 10 dB. Real-world experiments reveal validity of the proposed method." @default.
• W2921138327 created "2019-03-22" @default.
• W2921138327 creator A5080791396 @default.
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• W2921138327 date "2019-03-01" @default.
• W2921138327 modified "2023-10-15" @default.
• W2921138327 title "Speaker Tracking Based on Distributed Particle Filter and Iterative Covariance Intersection in Distributed Microphone Networks" @default.
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• W2921138327 doi "https://doi.org/10.1109/jstsp.2019.2903492" @default.
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