The Chamber Ensemble Generator: Limitless High-Quality MIR Data via Generative Modeling. (arXiv:2209.14458v1 [cs.SD])

Data is the lifeblood of modern machine learning systems, including for those
in Music Information Retrieval (MIR). However, MIR has long been mired by small
datasets and unreliable labels. In this work, we propose to break this
bottleneck using generative modeling. By pipelining a generative model of notes
(Coconet trained on Bach Chorales) with a structured synthesis model of chamber
ensembles (MIDI-DDSP trained on URMP), we demonstrate a system capable of
producing unlimited amounts of realistic chorale music with rich …

arxiv data ensemble generator modeling quality