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Evaluating Deep Music Generation Methods Using Data Augmentation. (arXiv:2201.00052v1 [cs.SD])
Jan. 4, 2022, 2:10 a.m. | Toby Godwin, Georgios Rizos, Alice Baird, Najla D. Al Futaisi, Vincent Brisse, Bjoern W. Schuller
cs.LG updates on arXiv.org arxiv.org
Despite advances in deep algorithmic music generation, evaluation of
generated samples often relies on human evaluation, which is subjective and
costly. We focus on designing a homogeneous, objective framework for evaluating
samples of algorithmically generated music. Any engineered measures to evaluate
generated music typically attempt to define the samples' musicality, but do not
capture qualities of music such as theme or mood. We do not seek to assess the
musical merit of generated music, but instead explore whether generated samples …
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