Robust MUSIC-Based Sound Source Localization in Reverberant and Echoic Environments

Intuitive human robot interfaces like speech or gesture recognition are essential for gaining acceptance for robots in daily life. However, such interaction requires that the robot detects the human’s intention to interact, tracks his position and keeps its sensor systems in an optimal configuration. Audio is a suitable modality for such task as it allows for detecting a speaker in arbitrary positions around the robot. In this paper, we present a novel approach for localization of sound sources by analyzing the frequency spectrum of the received signal and applying a motion model to the estimation process. We use an improved version of the Generalized Singular Value Decomposition (GSVD) based MUltiple SIgnal Classification (MUSIC) algorithm as a direction of arrival (DoA) estimator. Further, we introduce a motion model to enable robust localization in reverberant and echoic environments.We evaluate the system under real conditions in an experimental setup. Our experiments show that our approach outperforms current state-of-the-art algorithm and demonstrate the robustness against the previously mentioned disruptive factors.