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

Abstract

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.

Keywords

• acoustic generators
• acoustic signal processing
• direction‐of‐arrival estimation
• reverberation
• time‐frequency analysis
• transfer functions