Multiple Sound Source Counting and Localization Based on TF-Wise Spatial Spectrum Clustering

麦克风阵列 计算机科学 混响 模式识别(心理学) 空间分析 聚类分析 声源定位 话筒 高光谱成像 空间相关性 人工智能 语音识别 数学 声学 物理 统计 声音(地理) 电信 声压
作者
Bing Yang,Hong Liu,Cheng Pang,Xiaofei Li
出处
期刊:IEEE/ACM transactions on audio, speech, and language processing [Institute of Electrical and Electronics Engineers]
卷期号:27 (8): 1241-1255 被引量:22
标识
DOI:10.1109/taslp.2019.2915785
摘要

This paper addresses the problem of multiple sound source counting and localization in adverse acoustic environments, using microphone array recordings. The proposed time-frequency (TF) wise spatial spectrum clustering based method contains two stages. First, given the received sensor signals, the spatial correlation matrix is computed and denoised in the TF domain. The TF-wise spatial spectrum is estimated based on the signal subspace information, and further enhanced by an exponential transform, which can increase the reliability of the source presence possibility reflected by spatial spectrum. Second, to jointly count and localize sound sources, the enhanced TF-wise spatial spectra are divided into several clusters with each cluster corresponding to one source. Sources are successively detected by searching the significant peaks of the remaining global spatial spectrum, which is formed using unassigned spatial spectra. After each new source detection, spatial spectra are reassigned to detected sources according to the dominance association between them. The interaction between sources is reduced by iteratively performing new source detection and spatial spectrum assignment. Experiments on both simulated data and real-world data demonstrate the superiority of the proposed method for multiple sound source counting and localization in the environment with different levels of noise and reverberation.
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