Reverberant Sound Localization with a Robot Head Based on Direct-Path Relative Transfer Function

TL;DR

This paper presents a novel method for localizing speech sources with a robot head in reverberant environments by estimating the direct-path relative transfer function from noisy signals, improving accuracy over existing methods.

Contribution

The authors introduce a new approach to estimate the direct-path relative transfer function in noisy, reverberant settings, enhancing sound-source localization for robots.

Findings

Outperforms two state-of-the-art SSL methods in experiments

Effective in various reverberant environments

Uses DP-RTF as a robust feature for localization

Abstract

This paper addresses the problem of sound-source localization (SSL) with a robot head, which remains a challenge in real-world environments. In particular we are interested in locating speech sources, as they are of high interest for human-robot interaction. The microphone-pair response corresponding to the direct-path sound propagation is a function of the source direction. In practice, this response is contaminated by noise and reverberations. The direct-path relative transfer function (DP-RTF) is defined as the ratio between the direct-path acoustic transfer function (ATF) of the two microphones, and it is an important feature for SSL. We propose a method to estimate the DP-RTF from noisy and reverberant signals in the short-time Fourier transform (STFT) domain. First, the convolutive transfer function (CTF) approximation is adopted to accurately represent the impulse response of the microphone array, and the first coefficient of the CTF is mainly composed of the direct-path ATF. At each frequency, the frame-wise speech auto- and cross-power spectral density (PSD) are obtained by spectral subtraction. Then a set of linear equations is constructed by the speech auto- and cross-PSD of multiple frames, in which the DP-RTF is an unknown variable, and is estimated by solving the equations. Finally, the estimated DP-RTFs are concatenated across frequencies and used as a feature vector for SSL. Experiments with a robot, placed in various reverberant environments, show that the proposed method outperforms two state-of-the-art methods.