Robust Sound Source Localization considering Similarity of Back-Propagation Signals
This work addresses the problem of accurate sound source localization in noisy, cluttered environments for applications like robotics or audio processing, representing a strong specific gain rather than a foundational advancement.
The paper tackles robust sound source localization by estimating propagation paths via backward ray tracing and validating candidate positions using similarity of back-propagation signals, achieving an average error of 0.51 m in a 7 m by 7 m room and 65% to 220% accuracy improvement over state-of-the-art methods in noisy environments.
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.