Domínguez Morales, Juan PedroJiménez Fernández, Ángel FranciscoRíos Navarro, José AntonioCerezuela Escudero, ElenaGutiérrez Galán, DanielDomínguez Morales, Manuel JesúsJiménez Moreno, Gabriel2019-12-272019-12-272016Domínguez Morales, J.P., Jiménez Fernández, Á.F., Rios Navarro, A., Cerezuela Escudero, E., Gutiérrez Galán, D., Domínguez Morales, M.J. y Jiménez Moreno, G. (2016). Multilayer Spiking Neural Network for Audio Samples Classification Using SpiNNaker. En ICANN 2016: 25th International Conference on Artificial Neural Networks (45-53), Barcelona, España: Springer.978-3-319-44777-30302-9743https://hdl.handle.net/11441/91272Audio 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.application/pdfengAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/SpiNNakerSpiking neural networkAudio samples classificationSpikesNeuromorphic auditory sensorAddress-Event Representationinfo:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccesshttps://doi.org/10.1007/978-3-319-44778-0_6