Electronic dance music (EDM), produced using computers and electronic instruments, is a collection of musical sub-genres that emphasise timbre and rhythm over melody and harmony. It is usually presented through the medium of DJing, where tracks are curated and mixed sequentially to offer unique listening and dancing experiences. However, unlike key and tempo annotations, DJs still rely on audition rather than metadata to examine and select tracks with complementary audio content. In this work, we investigate the use of deep learning-based representations (Complex Autoencoder and OpenL3) for analysing and visualising audio content on a corpus of DJ mixes with approximate transition timestamps and compare them with signal processing-based representations (joint time-frequency scattering transform and mel-frequency cepstral coefficients). Representations are computed once per second and visualised with UMAP dimensionality reduction. We propose heuristics based on the identification of observed patterns in visualisations and time-sensitive Euclidean distances in the representation space to compute DJ transition lengths, transition smoothness, and inter-song, song-to-song, and full-mix audio content consistency using audio representations along with rough DJ transition timestamps. Our method enables the visualisation of variations within music tracks, facilitating the analysis of DJ mixes and individual EDM tracks. This approach supports musicians in making informed creative decisions based on such visualisations. We share our code, dataset annotations, computed audio representations, and trained CAE model. We encourage researchers and music enthusiasts alike to analyse their own music using our tools: https://github.com/alexjameswilliams/EDMAudioRepresentations.
(See document for exact affiliation information.)
https://aes2.org/publications/elibrary-page/?id=22438
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