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

TL;DR

Several enhancements to the LSDD algorithm are developed following a comprehensive performance and system analysis, which enable improved DOA estimation under these challenging conditions, including incorporating a new reliability weight and a new cluster quality measure into the algorithm.

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.

Figures (9)

• Figure 1: Flowchart showing the main processing blocks for DOA estimation using LSDD-base algorithm in a dynamic environment.
• Figure 2: Flowchart showing the main processing blocks for DOA estimation calculation in a dynamic environment using the $\text{LSDD-wQ}$ algorithm.
• Figure 3: Illustration of the AR glasses with locations of microphones donley2021easycom. Four of the microphones are fixed rigidly to the glasses and two of the microphones are placed in the user's ears.
• Figure 4: Array directivity as measured by Eq. (\ref{['EQN_STEERING+VECT_SIM']}), with $\phi_h=0^\circ$.
• Figure 5: Shows $\overline{n}_{\text{low}}$ as a function of $\lambda$ and for smoothing filters $U11$, $U33$, $U37$, $U57$.