Generate synthetic datasets for Morse code symbol classification for machine learning algotihms such as artificial neural networks.
Compute the inherent difficulty of the classification problem on these datasets using different metrics.
This research paper has more details. Please consider citing it if you use or benefit from this work:
S. Dey, K. M. Chugg and P. A. Beerel, “Morse Code Datasets for Machine Learning,” in 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-7, Jul 2018. (Won Best Paper Award)
Available on IEEE and arXiv (copyright owned by IEEE).
For a short description of dataset generation, see Sourya Dey's blog post
For a guide to Morse code symbols, see morse_tree. Go left for dots and right for dashes.
This family of datasets now has its own IEEEDataPort page and competition (open till October 14, 2020!)
Requirements: Python 3, numpy, scipy
Description of generate_morse_dataset.py:
Create morse code datasets:
Style='BW': Black and white having 0s for spaces and 1s for dot or dash. Noise means bit flips
Style='GRAY': Gaussian grayscale levels. Noise is additive Gaussian
save_filename: Leave as '' to save in preset path inside
Framelen: Total length of frame for 1 character
Classes: How many characters
TReach,VAeach,TEeach: No. of training, validation and test cases FOR EACH class
minlendot,maxlendot,... : min and max length of dots, dashes and intermediate spaces
leadingsp_rand: Set to 0 to have no leading spaces, otherwise 1 to have random number of leading spaces
dilation: If >1, all lengths are increased by this factor
Black and white only:
maxflip: Noise measure. Max how many bits to flip
SET maxflip=0 for NO NOISE
Grayscale only:
levels: How many levels for dots and dashes. Will be normalized at end
symbmean: Mean level for any symbol (dot or dash)
symbsd: Standard deviation for symbol levels
noisemean: Mean level for noise
noisesd: Standard deviation for noise
SET noisesd=0 for NO NOISE
2 already generated datasets are included:
- baseline.npz : Uses default parameters
- difficult.npz : Uses noisesd=4, leadingsp_rand=1, minlendash=3
Use load_data to extract the data and labels into training, validation and test:
xtrain, ytrain, xval, yval, xtest, ytest = load_data(filename = './baseline.npz')
Run dataset_metrics to test dataset difficulty, for example:
L, U, D, T = dataset_metrics('./baseline.npz')