Incremental Averaging Method to Improve Graph-Based Time-Difference-of-Arrival Estimation

TL;DR

This paper introduces an incremental averaging approach for GCC-PHAT functions in graph-based TDOA estimation, enhancing accuracy in noisy, reverberant environments by leveraging multiple spectral density averages.

Contribution

It proposes a novel incremental averaging method for GCC-PHAT functions that improves TDOA and source localization accuracy under challenging acoustic conditions.

Findings

Improved TDOA estimation accuracy in noisy environments.

Enhanced 2D source localization precision.

Robust performance across various microphone configurations.

Abstract

Estimating the position of a speech source based on time-differences-of-arrival (TDOAs) is often adversely affected by background noise and reverberation. A popular method to estimate the TDOA between a microphone pair involves maximizing a generalized cross-correlation with phase transform (GCC-PHAT) function. Since the TDOAs across different microphone pairs satisfy consistency relations, generally only a small subset of microphone pairs are used for source position estimation. Although the set of microphone pairs is often determined based on a reference microphone, recently a more robust method has been proposed to determine the set of microphone pairs by computing the minimum spanning tree (MST) of a signal graph of GCC-PHAT function reliabilities. To reduce the influence of noise and reverberation on the TDOA estimation accuracy, in this paper we propose to compute the GCC-PHAT functions of the MST based on an average of multiple cross-power spectral densities (CPSDs) using an incremental method. In each step of the method, we increase the number of CPSDs over which we average by considering CPSDs computed indirectly via other microphones from previous steps. Using signals recorded in a noisy and reverberant laboratory with an array of spatially distributed microphones, the performance of the proposed method is evaluated in terms of TDOA estimation error and 2D source position estimation error. Experimental results for different source and microphone configurations and three reverberation conditions show that the proposed method considering multiple CPSDs improves the TDOA estimation and source position estimation accuracy compared to the reference microphone- and MST-based methods that rely on a single CPSD as well as steered-response power-based source position estimation.