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Critical analysis reveals persistent data and algorithmic hurdles in modeling traditional music with deep learning, highlighting the need to diversify beyond Western popular genres.
Numerous applications for music listeners, educators, DJs, and musicians have been created over the past decade as the field of Music Deep Learning has expanded. Evidently, the majority of works rely on training their architectures using well-established databases, which consist primarily of Western popular music genres. This creates a deficiency in applications focusing on traditional and regional music, which are vital to preserving culture. As an increasing number of research groups begin to develop works centered on traditional music, several practical challenges for applying deep learning arise. This work discusses these issues and the future of deep learning in the study of traditional music.
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Moysis et al. (2023) studied this question.
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