In this paper, we explore the capabilities of a sound classification
system that combines a Neuromorphic Auditory System for feature extraction
and an artificial neural network for classification. Two models of neural network
have been used: Multilayer Perceptron Neural Network and Spiking Neural
Network. To compare their accuracies, both networks have been developed and
trained to recognize pure tones in presence of white noise. The spiking neural
network has been implemented in a FPGA device. The neuromorphic auditory
system that is used in this work produces a form of representation that is analogous
to the spike outputs of the biological cochlea. Both systems are able to distinguish
the different sounds even in the presence of white noise. The recognition system
based in a spiking neural networks has better accuracy, above 91 %, even when
the sound has white noise with the same power.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
