Audio classification has always been an interesting subject of research
inside the neuromorphic engineering field. Tools like Nengo or Brian, and hardware
platforms like the SpiNNaker board are rapidly increasing in popularity in
the neuromorphic community due to the ease of modelling spiking neural
networks with them. In this manuscript a multilayer spiking neural network for
audio samples classification using SpiNNaker is presented. The network consists
of different leaky integrate-and-fire neuron layers. The connections between them
are trained using novel firing rate based algorithms and tested using sets of pure
tones with frequencies that range from 130.813 to 1396.91 Hz. The hit rate
percentage values are obtained after adding a random noise signal to the original
pure tone signal. The results show very good classification results (above 85 %
hit rate) for each class when the Signal-to-noise ratio is above 3 decibels, validating
the robustness of the network configuration and the training step.Ministerio de Economía y Competitividad TEC2012-37868-C04-02Junta de Andalucía P12-TIC-130
