Multichannel Acoustic Beamforming for Speaker Localization and DOA-Based Tracking
TL;DR
Comparative results show that the proposed system provides competitive localization accuracy with a computationally simple processing pipeline, making it suitable for real-time embedded applications such as intelligent voice interfaces, videoconferencing systems, and service robotics.
Abstract
Real-time speaker localization and speech enhancement remain challenging for embedded acoustic systems operating in noisy and reverberant environments. This paper presents a multichannel beamforming framework based on Generalized Cross-Correlation with Phase Transform (GCC-PHAT) for Direction of Arrival (DOA) estimation and Delay-and-Sum (DAS) beamforming using a compact four-element circular MEMS microphone array. The proposed approach incorporates DOA smoothing and verification to improve localization stability before beamforming. Experimental evaluation in a controlled indoor environment demonstrated localization errors below 1°, while the beamforming stage achieved Signal-to-Noise Ratio (SNR) values predominantly between 25 dB and 40 dB, with peaks approaching 45 dB, and Root Mean Square Error (RMSE) values below 0.05 for most processing windows with an average processing time per frame of 3.473 ms and a memory consumption of 67.11 KB. Comparative results show that the proposed system provides competitive localization accuracy with a computationally simple processing pipeline, making it suitable for real-time embedded applications such as intelligent voice interfaces, videoconferencing systems, and service robotics.