Title: Morse Code Datasets for Machine Learning,
Abstract: We present an algorithm to generate synthetic datasets of tunable difficulty
on classification of Morse code symbols for supervised machine learning
problems, in particular, neural networks. The datasets are spatially
one-dimensional and have a small number of input features, leading to high
density of input information content. This makes them particularly challenging
when implementing network complexity reduction methods. We explore how network
performance is affected by deliberately adding various forms of noise and
expanding the feature set and dataset size. Finally, we establish several
metrics to indicate the difficulty of a dataset, and evaluate their merits. The
algorithm and datasets are open-source.