INDOOR MULTI-SOUND SOURCE LOCALIZATION BASED ON NONPARAMETRIC BAYESIAN CLUSTERING

被引：0

作者：

Guo, Yao ^{[1
]}

Zhu, Hongyan ^{[1
]}

Cheng, Qi ^{[2
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Shaanxi, Peoples R China

[2] Oklahoma State Univ, Sch Elect & Comp Engn, Stillwater, OK 74078 USA

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2017年

基金：

美国国家科学基金会; 中国国家自然科学基金;

关键词：

DOA; short time Fourier Transform (STFT); SVM; source number estimation; DPMM;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

This paper deals with sound source localization and number estimation in indoor environments using a circular microphone array. Multiple sound source localization is achieved by performing single source localization at each selected time-frequency (TF) point of received signals after short-time Fourier transform. A TF point selection method is proposed to reduce the computational time, which depends on a trained SVM with power and power ratio of TF points as its features. Nonparametric Bayesian clustering is applied on the obtained DOA estimates to identify the number of active sources. The algorithm is shown to outperform others through simulations.

引用

页码：6135 / 6139

页数：5

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