3D Sound Source Identification Based on Multi-Layer Perceptron and Grid-Free Strategy

Abstract:

In the past， the potential sound source area was divided into mounts of grids according to the beamforming algorithm， and all the sound sources were mapped into the grid points， which would lead to an incorrect sound source localization and intensity calculation， and the calculation accuracy and efficiency were affected by the size of grid spacing. In this paper， the multi-layer perceptron neural network and grid-free strategy are used to improve the spatial resolution and computational efficiency of sound source identification. Compared with the conventional cross-spectrum algorithm， with the algorithm of multi-layer perception， the spatial resolution can be improved in the depth direction as a planar array was applied to identify and localize two- point sound sources with the same intensity. In addition， multi-layer perceptron is superior to the conventional cross-spectrum algorithm in positioning error. Meanwhile， the intensity error of sound source identification is reduced. Moreover， multi-layer perceptron is superior to the beamforming algorithm at a low frequency range， which can be used as compensation for the poor spatial resolution of beamforming algorithm at this range.