Coherence-Based Frequency Subset Selection For Binaural RTF-Vector-Based Direction of Arrival Estimation for Multiple Speakers

TL;DR

This paper extends a single-speaker DOA estimation method to multiple speakers by selecting frequency subsets based on coherence measures, improving localization accuracy in reverberant noisy environments.

Contribution

It introduces a frequency subset selection approach for multi-speaker DOA estimation using Hermitian angle spectrum and coherence-based criteria.

Findings

Using binaural effective-coherence-based CDR yields best performance.

Frequency subset selection improves multi-speaker localization accuracy.

Method effective in reverberant environments with diffuse noise.

Abstract

Recently, a method has been proposed to estimate the direction of arrival (DOA) of a single speaker by minimizing the frequency-averaged Hermitian angle between an estimated relative transfer function (RTF) vector and a database of prototype anechoic RTF vectors. In this paper, we extend this method to multi-speaker localization by introducing the frequency-averaged Hermitian angle spectrum and selecting peaks of this spatial spectrum. To construct the Hermitian angle spectrum, we consider only a subset of frequencies, where it is likely that one speaker is dominant. We compare the effectiveness of the generalized magnitude squared coherence and two coherent-to-diffuse ratio (CDR) estimators as frequency selection criteria. Simulation results for estimating the DOAs of two speakers in a reverberant environment with diffuse-like babble noise using binaural hearing devices show that using the binaural effective-coherence-based CDR estimate as a frequency selection criterion yields the best performance.