Methodology for constructing a software-hardware system for localizing acoustic signals

This article proposes a methodology for constructing a software-hardware system for solving the problem of sound source localization in real acoustic environments based on the synthesis of several models, including intelligent ones. To achieve this goal, a methodology is proposed that includes the selection of technologies and equipment for collecting and processing acoustic signals, training models on experimental data, and conducting an additional series of experiments to evaluate the effectiveness of the models. Two models were considered: SI-GCC-CNN (Sound Intensity – Generalized Cross-Correlation Convolutional Neural Network), which combines sound intensity features and a generalized crosscorrelation phase transform as input to convolutional neural networks, and SI-CNN (Sound Intensity – Convolutional Neural Network), which feeds sound intensity features into a convolutional neural network. To evaluate the effectiveness of the deep learning models used to solve this problem at a spatial resolution of 10º, a series of experiments were conducted in closed reverberant rooms. The generalization ability of these models was assessed by varying configuration settings. The experimental results demonstrated the effectiveness and generalization ability of the SI-GCC-CNN model when working in real-world acoustic environments. The SI-GCC-CNN model outperformed the SI-CNN model, achieving a 2.9x improvement in localization accuracy when changing the room size, 2.5x when changing the distance between the source and the center of the microphone array, and 2x when changing the location of the microphone array.