A New Regional Localization Method for Indoor Sound Source Based on Convolutional Neural Networks

Abstract

At present, the sound source localization methods based on microphone arrays can be roughly classified into three categories: the controllable beamforming technology based on a maximum output power, the high-resolution spectrogram estimation technique, and the sound source localization technique based on time difference of sound. However, an existing localization technology in unstructured indoor environment lacks of localization accuracy and adaptability. In some practical situations, the location of sound source is limited to predefined areas. In this paper, we propose a research method of source region location system based on convolutional neural networks (CNNs). Based on the characteristics of weighted values of CNN, we realize the regional of indoor single sound sources transforming the sound source signals into grammar diagrams and then inputting them into the CNN. The whole process is based on the characteristics of weighted values of CNN. Finally, this paper completes the training and testing for CNN by using the Tensorflow framework. Simulation experiments on the test sets show the effectiveness of the proposed method.