Proceedings of the International Society for Music Information Retrieval
Abstract:
The “groove” of a song correlates with enjoyment and bodily
movement. Recent work has shown that humans often agree whether a song
does or does not have groove and how much groove a song has. It is
therefore useful to develop algorithms that characterize the quality
of groove across songs. We evaluate three unsupervised tempo-invariant
models for measuring pairwise musical groove similarity: A temporal
model, a timbre-temporal model, and a pitch-timbre-temporal model. The
temporal model uses a rhythm similarity metric proposed by Holzapfel
and Stylianou, while the timbre-inclusive models are built on shift
invariant probabilistic latent component analysis. We evaluate the
models using a dataset of over 8000 real-world musical recordings
spanning approximately 10 genres, several decades, multiple meters, a
large range of tempos, and Western and non-Western localities. A blind
perceptual study is conducted: given a random music query, humans rate
the groove similarity of the top three retrievals chosen by each of
the models, as well as three random retrievals.
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