Research on Sound Source Localization of Multiple Fixed Targets Based on Machine Learning and Distributed Arrays

Abstract

This paper studies a sound source localization method of multiple fixed targets based on machine learning and distributed arrays. In an outdoor open field, a three-line array was applied to collect array data and calculate latency characteristics. Then multiple classification models were established and trained. Finally, the locations of the sound source points were predicted by those models, in which the support vector machine (SVM), the nearest node (KNN), and the naive Bayesian model achieved 100% localization accuracy. Compared to the conventional method, this method has three significant advantages: First, it does not rely on the microphone channel order and does not need to be calibrated in advance, which simplifies the localization process; Second, it can fulfill high accuracy requirements, especially suitable for the scene of multiple fixed targets; Third, it has the advantage of incremental learning, as the times of localization rises, the training set is continuously enriched and the localization results become more precise.