speech_spike_signatures

Implementation of the paper A Spiking Network that Learns to Extract Spike Signatures from Speech Signals in PyGeNN: A Python Library for GPU-Enhanced Neural Networks

Steps

1. Install PyGeNN.
2. Install dependencies in requirements.txt.
3. Download and extract the Free Spoken Digit Dataset (FSDD).
4. Run src/preprocess.py to pre-process the data and extract features from it.
5. Run src/train.py to simulate (train) the spiking neural network (SNN).

Usage

preprocess.py

To pre-process the data and perform feature extraction using the preprocess.py script -

usage: Script to pre-preocess the speech data [-h] --data_dir DATA_DIR [--data_file DATA_FILE] [--upper_bound UPPER_BOUND] [--lower_bound LOWER_BOUND]

required arguments:
  --data_dir DATA_DIR         Should point to the the folder containing all the audio .wav files

optional arguments:
  --data_file DATA_FILE       Name of the .npy file the pre-processed should be saved as (default='data')
  --upper_bound UPPER_BOUND   The upper bound of for scaling the feature extracted from the speech signal (default=52000.0)
  --lower_bound LOWER_BOUND   The lower bound of for scaling the feature extracted from the speech signal (default=52.0)

Example arguments -

python preprocess.py --data_dir FSDD/recordings --data_file data --upper_bound 52000.0 --lower_bound 52.0

train.py

To train (simulate) the SNN using the train.py script -

usage: Script to train model the SNN using supervised STDP [-h] --datafile DATAFILE [--outdir OUTDIR] [--n_samples N_SAMPLES]

required arguments:
  --datafile DATAFILE        Path to the .npy file containing the speech data

optional arguments:
  --outdir OUTDIR            Name of folder where all the ouput files (membrane potentials etc) should be stored. Folder doesn't need to exist beforehand. (default='output')
  --n_samples N_SAMPLES      Number of samples in the dataset for which the network should be simulated. (default=1)

Example arguments -

python train.py --datafile data/data_52000.npy --outdir results --n_samples 1