We demonstrate a data-driven unsupervised approach for
the discovery of melodic patterns in large collections of
Indian art music recordings. The approach first works on
single recordings and subsequently searches in the entire
music collection. Melodic similarity is based on dynamic
time warping. The task being computationally intensive,
lower bounding and early abandoning techniques are applied
during distance computation. Our dataset comprises
365 hours of music, containing 1,764 audio ...
We demonstrate a data-driven unsupervised approach for
the discovery of melodic patterns in large collections of
Indian art music recordings. The approach first works on
single recordings and subsequently searches in the entire
music collection. Melodic similarity is based on dynamic
time warping. The task being computationally intensive,
lower bounding and early abandoning techniques are applied
during distance computation. Our dataset comprises
365 hours of music, containing 1,764 audio recordings representing
the melodic diversity of Carnatic music. A preliminary
evaluation based on expert feedback on a subset
of the music collection shows encouraging results. In particular,
several musically interesting relationships are discovered,
yielding further scope for establishing novel similarity
measures based on melodic patterns.
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