Learning Deep Direct-Path Relative Transfer Function for Binaural Sound Source Localization

TL;DR

This paper introduces a deep learning approach to robustly estimate the direct-path relative transfer function (DP-RTF) for binaural sound source localization, improving accuracy in noisy and reverberant environments.

Contribution

It proposes a novel neural network architecture that jointly learns inter-channel features and enhances speech spectra, enabling generalization across different binaural arrays without retraining.

Findings

Effective in noisy and reverberant conditions

Generalizes well to unseen binaural arrays

Improves direction of arrival estimation accuracy

Abstract

Direct-path relative transfer function (DP-RTF) refers to the ratio between the direct-path acoustic transfer functions of two microphone channels. Though DP-RTF fully encodes the sound spatial cues and serves as a reliable localization feature, it is often erroneously estimated in the presence of noise and reverberation. This paper proposes to learn DP-RTF with deep neural networks for robust binaural sound source localization. A DP-RTF learning network is designed to regress the binaural sensor signals to a real-valued representation of DP-RTF. It consists of a branched convolutional neural network module to separately extract the inter-channel magnitude and phase patterns, and a convolutional recurrent neural network module for joint feature learning. To better explore the speech spectra to aid the DP-RTF estimation, a monaural speech enhancement network is used to recover the direct-path spectrograms from the noisy ones. The enhanced spectrograms are stacked onto the noisy spectrograms to act as the input of the DP-RTF learning network. We train one unique DP-RTF learning network using many different binaural arrays to enable the generalization of DP-RTF learning across arrays. This way avoids time-consuming training data collection and network retraining for a new array, which is very useful in practical application. Experimental results on both simulated and real-world data show the effectiveness of the proposed method for direction of arrival (DOA) estimation in the noisy and reverberant environment, and a good generalization ability to unseen binaural arrays.