Indoor sound source localization under reverberation by extracting the features of sample covariance

Abstract

Three dimensional indoor sound source localization is well known as a challenging task due to the complicated mechanics of reverberation. The conventional model-based methods generally require the prior knowledge of the microphone array geometry, the environment’s parameters, and the statistics of the signal and noise. On the other hand, the data-based methods could work in the absence of all/part of the aforementioned prior knowledge, while the localization performance depends on the established features of the data and the nature of neural network. In this work, a feature vector constructed from the sample covariance matrix of the microphone array data is fed to a designed deep neural network. Numerical simulation results show that the proposed method outperforms [21] in both ranging and direction finding, hence locating a human-speech source in a three-dimensional reverberant room space. An average relative localization error below 3% can be reached, which is robust to the room reverberation level of T60 from 0 to 600 ms when the signal-to-noise-ratio is not below 0 dB. Moreover, the robustness of the proposed method against different scenarios is verified: the increment of relative localization error is below 5% for various room dimensions, below 10% for different speech signals, and below 3% for microphone array offsets.