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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References
Lichtsteiner, P., Posch, C., Delbruck, T.: A 128 × 128 120 dB 15 μs latency asynchronous temporal contrast vision sensor. IEEE J. Solid-State Circ. 43, 566–576 (2008)
Chan, V., Liu, S.C., van Schaik, A.: AER EAR: a matched silicon cochlea pair with address event representation interface. IEEE Trans Circ. Syst. I 54(1), 48–59 (2007)
Häfliger, P.: Adaptive WTA with an analog VLSI neuromorphic learning chip. IEEE Trans. Neural Netw. 18, 551–572 (2007)
Indiveri, G., Chicca, E., Douglas, R.: A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity. IEEE Trans. Neural Netw. 17, 211–221 (2006)
Jiménez-Fernández, A., Jiménez-Moreno, G., Linares-Barranco, A., et al.: Building blocks for spikes signal processing. In: International Joint Conference on Neural Networks, IJCNN (2010)
Linares-Barranco, A., et al.: A USB3.0 FPGA event-based filtering and tracking framework for dynamic vision sensors. In: Proceedings of IEEE International Symposium on Circuits and Systems, pp. 2417–2420 (2015)
Linares-Barranco, A., Gomez-Rodriguez, F., Jimenez-Fernandez, A., et al.: Using FPGA for visuo-motor control with a silicon retina and a humanoid robot. In: IEEE International Symposium on Circuits and Systems, pp. 1192–1195 (2007)
Jimenez-Fernandez, A., Jimenez-Moreno, G., Linares-Barranco, A., et al.: A neuro-inspired spike-based PID motor controller for multi-motor robots with low cost FPGAs. Sensors 12, 3831–3856 (2012)
Hamilton, T.J., Jin, C., van Schaik, A., Tapson, J.: An active 2-D silicon cochlea. IEEE Trans. Biomed. Circ. Syst. 2, 30–43 (2008)
Jimenez-Fernandez, A., Cerezuela-Escudero, E., Miro-Amarante, L., et al.: A binaural neuromorphic auditory sensor for FPGA: a spike signal processing approach. IEEE Trans. Neural Networks Learn. Syst. 1(0) (2016)
Boahen, K.: Point-to-point connectivity between neuromorphic chips using address events. IEEE Trans. Circ. Syst II Analog Digit Sig. Process. 47, 416–434 (2000)
Bekolay, T., et al.: Nengo: a Python tool for building large-scale functional brain models. Front Neuroinform. 7, 48 (2014)
Goodman, D., Brette, R.: Brian: a simulator for spiking neural networks in python. Front Neuroinform. 2, 5 (2008)
jAER Open Source Project. http://jaer.wiki.sourceforge.net
Berner, R., Delbruck, T., Civit-Balcells, A., Linares-Barranco, A.: A 5 Meps $100 USB2.0 address-event monitor-sequencer interface. IEEE International Symposium on Circuits and Systems (2007)
Painkras, E., et al.: SpiNNaker: A 1-W 18-core system-on-chip for massively-parallel neural network simulation. IEEE J. Solid-State Circ. 48, 1943–1953 (2013)
Davison, A.P.: PyNN: a common interface for neuronal network simulators. Front Neuroinform. 2, 11 (2008)
SpiNNaker Home Page. http://apt.cs.manchester.ac.uk/projects/SpiNNaker
Dominguez-Morales, J.P.: Multilayer spiking neural network for audio samples classification using Spinnaker Github page. https://github.com/jpdominguez/Multilayer-SNN-for-audio-samples-classification-using-SpiNNaker
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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