Indoor Sound Source Localization with Probabilistic Neural Network

It is known that adverse environments such as high reverberation and low signal-to-noise ratio (SNR) pose a great challenge to indoor sound source localization. To address this challenge, in this paper, we propose a sound source localization algorithm based on probabilistic neural network, namely Ge...

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Bibliographic Details
Published in:arXiv.org 2017-12
Main Authors: Sun, Yingxiang, Chen, Jiajia, Chau Yuen, Rahardja, Susanto
Format: Article
Language:English
Subjects:
Acoustic noise
Algorithms
Cross correlation
Direction of arrival
Elevation angle
Localization
Neural networks
Reverberation time
Sound localization
Online Access:Get full text
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Summary:It is known that adverse environments such as high reverberation and low signal-to-noise ratio (SNR) pose a great challenge to indoor sound source localization. To address this challenge, in this paper, we propose a sound source localization algorithm based on probabilistic neural network, namely Generalized cross correlation Classification Algorithm (GCA). Experimental results for adverse environments with high reverberation time T60 up to 600ms and low SNR such as -10dB show that, the average azimuth angle error and elevation angle error by GCA are only 4.6 degrees and 3.1 degrees respectively. Compared with three recently published algorithms, GCA has increased the success rate on direction of arrival estimation significantly with good robustness to environmental changes. These results show that the proposed GCA can localize accurately and robustly for diverse indoor applications where the site acoustic features can be studied prior to the localization stage.
ISSN:2331-8422
DOI:10.48550/arxiv.1712.07814