Deep Learning for Binaural Sound Source Localization with Low Signal-to-noise Ratio

Abstract

A novel deep learning (DL) method is proposed for binaural sound source localization with low SNR. Firstly, the binaural sound signals are decomposed into several channels by using Gammatone filter. Secondly, the 4 feature parameters of Head-related Transfer Function, interaural time difference (ITD), interaural coherence (IC), interaural level difference (ILD), and interaural phase difference (IPD) are extracted. Thirdly, ITD and IC go through a Deep Belief Network (DBN) to determine the quadrant of the sound source and reduce the positioning range. Then, ITD, IC, ILD, and IPD go through a Deep Neural Network (DNN) to obtain the azimuthal angle within 90 degrees. Experimental results show that the proposed algorithm can solve the front-back confusion, and obtain a superior performance with lower complexity and higher precision under low SNR conditions.