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.

本文重点研究声源检测技术。结合人工智能（AI）和第六代（6G）通信，构建集中式麦克风阵列以定位特定场景中的声源是一个热门话题。然而，阵列建立、定位实施及其性能之间的相关性和影响仍然存在一定程度的模糊性。为此，我们提出了一种具有高精度、超可靠性和低延迟通信的声源定位系统（SSLS）。同时，基于 AI 的反向传播 (BP) 神经网络使 SSLS 能够预测声源位置 (SSP)。SSLS主要由数据采集终端和数据处理终端组成。依赖Visual Studio，采用C/C#后，SSLS 提供可靠的 SSP 测量。SSLS运行时，通过阵列采集声源信号（SSS），实时获取到达时间差（TDOA）值。与现有方法不同，该SSLS通过检测具有固定能量强度的信号，将SSS变为脉冲信号。同时，设计了基于强度的触发中断来完成TDOA采集。这样，除了减少TDOA计算量外，信号识别也得到了提高。此外，在使用 TDOA 定位声源时，介绍了使用 BP 网络的定位步骤。考虑到极端声源角度，提出了一种基于多平面的数据融合方法。随着不同角度，尤其是极端角度的设定，我们评估了所提出方法的定量和定性结果，证明无论角度和平面数量如何，我们的方法都可以实现高精度定位结果。最后，比较实验证实，所提出的 SSLS 明显优于现有方法。