Indoor Sound Source Localization with Probabilistic Neural Network

Abstract

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.

10 pages, accepted by IEEE Transactions on Industrial Electronics

Cited by in corpus (5)

• Towards End-to-End Acoustic Localization using Deep Learning: from Audio Signal to Source Position Coordinates
• Robust Sound Source Tracking Using SRP-PHAT and 3D Convolutional Neural Networks
• Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs
• Multi-Source DOA Estimation through Pattern Recognition of the Modal Coherence of a Reverberant Soundfield
• A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods