Improved direction of arrival estimations with a wearable microphone array for dynamic environments by reliability weighting

TL;DR

This paper enhances multi-speaker direction-of-arrival estimation in dynamic, noisy, and reverberant environments using a wearable microphone array by improving the LSDD algorithm with reliability weighting and quality measures.

Contribution

It introduces a modified LSDD algorithm with reliability weighting and a new quality measure to improve DOA estimation in challenging real-world conditions.

Findings

Improved DOA accuracy in dynamic environments.

Enhanced robustness against noise and reverberation.

Effective performance on the EasyCom dataset.

Abstract

Direction-of-arrival estimation of multiple speakers in a room is an important task for a wide range of applications. In particular, challenging environments with moving speakers, reverberation and noise, lead to significant performance degradation for current methods. With the aim of better understanding factors affecting performance and improving current methods, in this paper multi-speaker direction-of-arrival (DOA) estimation is investigated using a modified version of the local space domain distance (LSDD) algorithm in a noisy, dynamic and reverberant environment employing a wearable microphone array. This study utilizes the recently published EasyCom speech dataset, recorded using a wearable microphone array mounted on eyeglasses. While the original LSDD algorithm demonstrates strong performance in static environments, its efficacy significantly diminishes in the dynamic settings of the EasyCom dataset. Several enhancements to the LSDD algorithm are developed following a comprehensive performance and system analysis, which enable improved DOA estimation under these challenging conditions. These improvements include incorporating a weighted reliability approach and introducing a new quality measure that reliably identifies the more accurate DOA estimates, thereby enhancing both the robustness and accuracy of the algorithm in challenging environments.