Robust Sound Source Localization considering Similarity of Back-Propagation Signals

TL;DR

This paper introduces a robust sound source localization method that uses back-propagation signal similarities and Monte Carlo localization, achieving high accuracy even in complex environments with obstacles and noise.

Contribution

The novel approach combines acoustic ray tracing, impulse response design, and signal similarity analysis for improved 3D source localization.

Findings

Achieved an average localization error of 0.51 meters.

Improved accuracy by 65% to 220% over existing methods.

Effective in environments with moving sources, obstacles, and noise.

Abstract

We present a novel, robust sound source localization algorithm considering back-propagation signals. Sound propagation paths are estimated by generating direct and reflection acoustic rays based on ray tracing in a backward manner. We then compute the back-propagation signals by designing and using the impulse response of the backward sound propagation based on the acoustic ray paths. For identifying the 3D source position, we suggest a localization method based on the Monte Carlo localization algorithm. Candidates for a source position is determined by identifying the convergence regions of acoustic ray paths. This candidate is validated by measuring similarities between back-propagation signals, under the assumption that the back-propagation signals of different acoustic ray paths should be similar near the sound source position. Thanks to considering similarities of back-propagation signals, our approach can localize a source position with an averaged error of 0.51 m in a room of 7 m by 7 m area with 3 m height in tested environments. We also observe 65 % to 220 % improvement in accuracy over the stateof-the-art method. This improvement is achieved in environments containing a moving source, an obstacle, and noises.