No abstract

The existing speech enhancement algorithm has shown poor performance under low Signal Noise Ratios (SNRs). To resolve this problem, a speech enhancement algorithm based on binaural sound source localization and cosh measure filtering is proposed. Firstly, the algorithm uses a sound source localization algorithm based on head correlation functions and two-level deep learning to extract the spatial information of the binaural sound source and determine the spatial position of the sound source. The beamforming method is then used to remove the noises in different directions from the speech. Finally, the Wiener filtering of cosh measure based on logarithmic relation is used to remove the noise in the same direction as the speech to achieve speech enhancement. Experiments show that the proposed algorithm has better robustness and denoising ability than the contrast algorithms.

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.