Enhancing direct-path relative transfer function using deep neural network for robust sound source localization

Abstract

This article proposes a deep neural network (DNN)-based direct-path relative transfer function (DP-RTF) enhancement method for robust direction of arrival (DOA) estimation in noisy and reverberant environments. The DP-RTF refers to the ratio between the direct-path acoustic transfer functions of the two microphone channels. First, the complex-value DP-RTF is decomposed into the inter-channel intensity difference, and sinusoidal functions of the inter-channel phase difference in the time-frequency domain. Then, the decomposed DP-RTF features from a series of temporal context frames are utilized to train a DNN model, which maps the DP-RTF features contaminated by noise and reverberation to the clean ones, and meanwhile provides a time-frequency (TF) weight to indicate the reliability of the mapping. The DP-RTF enhancement network can help to enhance the DP-RTF against noise and reverberation. Finally, the DOA of a sound source can be estimated by integrating the weighted matching between the enhanced DP-RTF features and the DP-RTF templates. Experimental results on simulated data show the superiority of the proposed DP-RTF enhancement network for estimating the DOA of the sound source in the environments with various levels of noise and reverberation.