Multi-Sound-Source Localization Using Machine Learning for Small Autonomous Unmanned Vehicles with a Self-Rotating Bi-Microphone Array

TL;DR

This paper introduces two machine learning-based methods for 3D multi-sound-source localization on small autonomous vehicles using a self-rotating bi-microphone array, outperforming traditional vision-based techniques in reverberant environments.

Contribution

It presents novel approaches using DBSCAN and RANSAC algorithms for sound-source localization with a self-rotating microphone array on SAUVs, demonstrating effective 3D localization in challenging environments.

Findings

Both methods accurately identify the number of sound sources.

They determine 3D orientations of sources in reverberant environments.

Performance is validated through simulations and experiments.

Abstract

Abstract While vision-based localization techniques have been widely studied for small autonomous unmanned vehicles (SAUVs), sound-source localization capabilities have not been fully enabled for SAUVs. This paper presents two novel approaches for SAUVs to perform three-dimensional (3D) multi-sound-sources localization (MSSL) using only the inter-channel time difference (ICTD) signal generated by a self-rotating bi-microphone array. The proposed two approaches are based on two machine learning techniques viz., Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Sample Consensus (RANSAC) algorithms, respectively, whose performances are tested and compared in both simulations and experiments. The results show that both approaches are capable of correctly identifying the number of sound sources along with their 3D orientations in a reverberant environment.