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

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 include incorporating a new reliability weight and a new cluster quality measure into the algorithm. The new reliability weight combines the existing data-based reliability with a novel array-based reliability while the new cluster quality measure reflects the likelihood that a DOA cluster represents a correct cluster due to a sound source and not a spurious cluster due to noise or reverberations. The use of the cluster quality measure significantly enhances both the robustness and accuracy of the DOA estimates in the tested dynamic and challenging environment.