Live demonstration — Multilayer spiking neural network for audio samples classification using SpiNNaker

Abstract

In this demonstration we present a spiking neural network architecture for audio samples classification using SpiNNaker. The network consists of different leaky integrate-and-fire neuron layers. The connections between them are trained using firing rate based algorithms. Tests use sets of pure tones with frequencies that range from 130.813 to 1396.91 Hz. Audio signals coming from the computer are converted to spikes using a Neuromorphic Auditory Sensor and, after that, this information is sent to the SpiNNaker board through a PCB that translates from AER to 2-of-7 protocol. The classification output obtained in the spiking neural network deployed on SpiNNaker is then shown in the computer screen. Different levels of random noise are added to the original audio signals in order to test the robustness of the classification system.