Phased Microphone Array for Sound Source Localization with Deep Learning
This work addresses the need for efficient and precise sound source localization, which is incremental as it adapts deep learning to an existing problem.
The paper tackles sound source localization using a phased microphone array by applying a convolutional neural network (CNN) as a new algorithm, achieving high spatial resolution comparable to DAMAS and computational efficiency similar to conventional beamforming at high frequencies.
To phased microphone array for sound source localization, algorithm with both high computational efficiency and high precision is a persistent pursuit. In this paper convolutional neural network (CNN) a kind of deep learning is preliminarily applied as a new algorithm. At high frequency CNN can reconstruct the sound localizations with excellent spatial resolution as good as DAMAS, within a very short time as short as conventional beamforming. This exciting result means that CNN perfectly finds source distribution directly from cross-spectral matrix without given propagation function in advance, and thus CNN deserves to be further explored as a new algorithm.