Particle filtering algorithms for tracking an acoustic source in a reverberant environment

TLDR

Traditional acoustic source localization algorithms rely on current‑time sensor data, but in strong multipath environments they often mistakenly identify a reflection instead of the true source; a promising state‑space particle‑filtering approach can mitigate this issue. The paper formulates a general framework for tracking an acoustic source using particle filters. Within this framework, four specific algorithms are presented and evaluated on both simulated reverberant data and recordings from a moderately reverberant office room with a 0.39‑second reverberation time. The results show that the proposed family of algorithms can accurately track a moving source in such a reverberant environment.

Abstract

Traditional acoustic source localization algorithms attempt to find the current location of the acoustic source using data collected at an array of sensors at the current time only. In the presence of strong multipath, these traditional algorithms often erroneously locate a multipath reflection rather than the true source location. A recently proposed approach that appears promising in overcoming this drawback of traditional algorithms, is a state-space approach using particle filtering. In this paper we formulate a general framework for tracking an acoustic source using particle filters. We discuss four specific algorithms that fit within this framework, and demonstrate their performance using both simulated reverberant data and data recorded in a moderately reverberant office room (with a measured reverberation time of 0.39 s). The results indicate that the proposed family of algorithms are able to accurately track a moving source in a moderately reverberant room.