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Signal-domain representation of symbolic music for learning embedding spaces
[article]
2021
arXiv
pre-print
A key aspect of machine learning models lies in their ability to learn efficient intermediate features. However, the input representation plays a crucial role in this process, and polyphonic musical scores remain a particularly complex type of information. In this paper, we introduce a novel representation of symbolic music data, which transforms a polyphonic score into a continuous signal. We evaluate the ability to learn meaningful features from this representation from a musical point of
arXiv:2109.03454v1
fatcat:qmjt5lfj7jaz3hyq256shnesj4