Abstract
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.
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Acknowledgements
The authors would like to thank the APT Research Group of the University of Manchester for instructing us in the SpiNNaker. This work is supported by the Spanish government grant BIOSENSE (TEC2012-37868-C04-02) and by the excellence project from Andalusian Council MINERVA (P12-TIC-1300), both with support from the European Regional Development Fund.
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Dominguez-Morales, J.P. et al. (2016). Multilayer Spiking Neural Network for Audio Samples Classification Using SpiNNaker. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_6
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DOI: https://doi.org/10.1007/978-3-319-44778-0_6
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