Archives of Acoustics, 46, 1, pp. 67–78, 2021
10.24425/aoa.2021.136561

Acoustic Source Localization Using Kernel-based Extreme Learning Machine in Distributed Microphone Array

Rong WANG
Dalian University of Technology
China

Zhe CHEN
Dalian University of Technology
China

Fuliang YIN
Dalian University of Technology
China

Acoustic source localization using distributed microphone array is a challenging task due to the influences of noise and reverberation. In this paper, acoustic source localization using kernel-based extreme learning machine in distributed microphone array is proposed. Specifically, the space of interest is divided into some labeled positions, and the candidate generalized cross correlation function in each node is treated as the feature mapped into the hidden nodes of extreme learning machine. During the training phase, by the implementation of kernel function, the output weights of the classifier are calculated and do not need to be tuned. After the kernel-based extreme learning machine (K-ELM) is well trained, the measured generalized cross correlation data are fed into the K-ELM classifier, and the output is the estimated acoustic source position. The proposed method needs less human intervention for both training and testing and it does not need to calibrate the node in advance. Simulation and real-world experimental results reveal that the proposed method has extremely fast training and testing speeds, and can obtain better localization performance than steered response power, K-nearest neighbor, and support vector machine methods.
Keywords: extreme learning machine; acoustic source localization; distributed microphone array; generalized cross correlation function
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DOI: 10.24425/aoa.2021.136561