AI-based sound source localization system with higher accuracy

Abstract

This paper focuses on the research of sound source detection technology. Combining the artificial intelligence (AI) and 6th generation (6G) communications, it is a hot topic to build a centralized microphone array to locate the sound source in a particular scene. However, degrees of vagueness still exist concerning the correlations and influences between the array establishment, localization implementation and its performance. To this end, we propose a sound source localization system (SSLS) with a high accuracy, ultra reliability and low latency communication. Meanwhile, an AI-based back propagation (BP) neural network enables the SSLS to predict the sound source position (SSP). SSLS is mainly composed of a data acquisition terminal and a data processing terminal. After relying on the Visual Studio and adopting the C/C#, a reliable SSP measurement is provided by the SSLS. When SSLS is running, sound source signals (SSSs) are collected through the array to obtain the time difference of arrival (TDOA) values in real time. Different from existing methods, this SSLS changes the SSS into a pulse signal by detecting the signal with a fixed energy intensity. At the same time, an intensity-based trigger interruption is designed to complete the TDOA acquisition. In this way, besides a reduction in TDOA calculation, the signal recognition is also improved. Further, while using TDOAs to locate the sound source, localization steps with a BP network are presented. Considering extreme sound source angles, a multi-plane based data fusion method is proposed. With the setting of different angles, especially extreme angles, we assess both quantitative and qualitative results of the proposed method, demonstrating that our method can achieve high-accuracy localization results regardless of angle degrees and the number of planes. Finally, comparison experiments confirm that the proposed SSLS significantly outperforms existing methods.