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论文类型：期刊论文

发表时间：2017-04-01

发表刊物：DIGITAL SIGNAL PROCESSING

收录刊物：SCIE、EI

卷号：63

页面范围：112-122

ISSN号：1051-2004

关键字：Speaker tracking; Distributed particle filter; Distributed microphone networks; Non-Gaussian noise; Generalized correntropy function

摘要：Non-Gaussian noise distorts the speech signals and leads to the degradation of speaker tracking performance. In this paper, a distributed particle filter (DPF) based speaker tracking method in distributed microphone networks under non-Gaussian noise and reverberant environments is proposed. A generalized correntropy function is first presented to estimate the time differences of arrival (TDOA) for speech signals at each node in distributed microphone networks. Next, to address spurious TDOA estimations caused by noise and reverberation, a multiple-hypothesis likelihood model is introduced to calculate the local likelihood functions of the DPF. Finally, a DPF fusing local likelihood functions with an average consensus algorithm is employed to estimate a moving speaker's positions. The proposed method can accurately track the speaker under non-Gaussian noise and reverberant environments, and it is scalable and robust against the nodes failure in distributed networks. Simulation experiments show the validation of the proposed speaker tracking method. (C) 2017 Elsevier Inc. All rights reserved.