Sound fields clusterization via neural networks

Abstract

Paper presents application of a recently proposed approach for multidimensional data clustering to data received from a microphone array antenna. The accumulated sound pressure at each point (a microphone in the array) is used to create “sound picture” of the observed by the microphone antenna area. Features for classification are extracted using overlapping receptive fields based on the model of direction selective cells in the middle temporal (MT) cortex. Next the clustering procedure using Echo state network and subtractive clustering algorithm is applied to separate receptive fields in proper number of classes. The obtained results are compared with the sonograms created by the original software of the producer of microphone array.