陈喆

个人信息Personal Information

教授

博士生导师

硕士生导师

性别:男

毕业院校:大连理工大学

学位:博士

所在单位:信息与通信工程学院

学科:信号与信息处理. 通信与信息系统

办公地点:大连理工大学创新园大厦A526室

联系方式:0411-84706005-3526

电子邮箱:zhechen@dlut.edu.cn

扫描关注

论文成果

当前位置: 中文主页 >> 科学研究 >> 论文成果

Distributed multiple speaker tracking based on time delay estimation in microphone array network

点击次数:

论文类型:期刊论文

发表时间:2020-12-01

发表刊物:IET SIGNAL PROCESSING

卷号:14

期号:9

页面范围:591-601

ISSN号:1751-9675

关键字:delay estimation; reverberation; microphone arrays; speaker recognition; Kalman filters; sensor fusion; distributed microphone array network; DMA; multiple speaker scenarios; ambiguous observation; noisy environments; distributed multiple speaker tracking method; time delay estimation strategy; reliable time delays; distributed Kalman filter framework

摘要:Multiple speaker tracking in distributed microphone array (DMA) network is a challenging task. A critical issue for multiple speaker scenarios is to distinguish the ambiguous observation and associate it to the corresponding speaker, especially under reverberant and noisy environments. To address the problem, a distributed multiple speaker tracking method based on time delay estimation in DMA is proposed in this study. Specifically, the time delay estimated by the generalised cross-correlation function is treated as an observation. In order to distinguish the observation for each speaker, the possible time delays, refer to as candidates, are extracted based on data association technique. Considering the ambient influence, a time delay estimation strategy is designed to calculate the time delay for each speaker from the candidates. Finally, only the reliable time delays in DMA are propagated throughout the whole network by diffusion fusion algorithm and used for updating the speakers' state within the distributed Kalman filter framework. The proposed approach can track multiple speakers successfully in a non-centralised manner under reverberant and noisy environments. Simulation results indicate that, compared with other methods, the proposed method can achieve a smaller root mean square error for multiple speaker tracking, especially in adverse conditions.