Phased microphone array for sound source localization with deep learning

Abstract

To phased microphone array for sound source localization, algorithm with both high computational efficiency and high precision is a persistent pursuit until now. In this paper, convolutional neural network (CNN) a kind of deep learning is preliminarily applied as a new algorithm. The input of CNN is only cross-spectral matrix, while the output of CNN is source distribution. With regard to computing speed in applications, CNN once trained is as fast as conventional beamforming, and is significantly faster than the most famous deconvolution algorithm DAMAS. With regard to measurement accuracy in applications, at high frequency, CNN can reconstruct the sound localizations with up to 100% test accuracy, although sidelobes may appear in some situations. In addition, CNN has a spatial resolution nearly as that of DAMAS and better than that of the conventional beamforming. CNN test accuracy decreases with frequency decreasing; however, in most incorrect samples, CNN results are not far away from the correct results. This exciting result means that CNN perfectly finds source distribution directly from cross-spectral matrix without given propagation function and microphone positions in advance, and thus, CNN deserves to be further explored as a new algorithm.