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

摘要

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 directpath 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.