The project aimed to model the auditory system using spiking neurons network (with the Nengo platform and the SpiNNaker chip). The spikes provided by the auditory system are analysed using a automatic speech recognition system which recognise five simple speech commands: go, stop, back, right and left. These commands are used to move a robot. The project was divided in three main parts. The first part was to model the human auditory system and map sound frequencies into spikes using the Nengo platform. The second one was to use these signals and recognise a few commands to move a robot with speech. Finally, the third part is a desktop application for recording the audio and sending it to the neural network to recognise the commands.

PyQt4 for python 2.7 https://www.riverbankcomputing.com/software/pyqt/download run the .exe and install

PyAudio (listen to mic on windows) pip install pyaudio

Pyqtgraph (work alongside pyqt4) pip install pyqtgraph

Nengo git clone https://github.com/nengo/nengo.git cd nengo python setup.py develop

SpiNNaker git clone https://github.com/ctn-waterloo/nengo_spinnaker cd nengo_spinnaker python setup.py develop nengo_spinnaker_setup 1 - Manually 2 - IP: spinn-9.cs.man.ac.uk

Robot: clone https://github.com/tcstewar/nstbot.git cd nstbot python setup.py install

To run the application run the audioApplication.py To train the model run trainModel.py To record more train samples run the recordAudio.py, then cut the sound as the samples in audio_samples and place it in the correct folder inside audio_samples folder.

Created by Leonardo Claudio de Paula e Silva

MIT License

Copyright (c) 2016 Leonardo Claudio de Paula e Silva

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