JS Fake Chorales: a Synthetic Dataset of Polyphonic Music with Human Annotation

Understand

High-quality datasets for learning-based modelling of polyphonic symbolic music remain less readily-accessible at scale than in other domains, such as language modelling or image classification.

• Deep learning algorithms show great potential for enabling the widespread use of interactive music generation technology in consumer applications, but the lack of large-scale datasets remains a bottleneck for the development of algorithms that can consistently generate high-quality outputs.
• We propose that models with narrow expertise can serve as a source of high-quality scalable synthetic data, and open-source the JS Fake Chorales, a dataset of 500 pieces generated by a new learning-based algorithm, provided in MIDI form.
• We take consecutive outputs from the algorithm and avoid cherry-picking in order to validate the potential to further scale this dataset on-demand.