Morse Code Datasets for Machine Learning
This provides a specialized tool for researchers testing neural network complexity reduction methods, but it is incremental as it focuses on a specific domain.
The authors tackled the problem of generating synthetic datasets for Morse code classification with tunable difficulty, resulting in open-source datasets and metrics to evaluate dataset difficulty.
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.