Discovery of repeating structures in music is
fundamental to its analysis, understanding and interpretation.
We present a data-driven approach for the discovery of shorttime
melodic patterns in large collections of Indian art music.
The approach first discovers melodic patterns within an audio
recording and subsequently searches for their repetitions in the
entire music collection. We compute similarity between melodic
patterns using dynamic time warping (DTW). Furthermore, we
investigate ...
Discovery of repeating structures in music is
fundamental to its analysis, understanding and interpretation.
We present a data-driven approach for the discovery of shorttime
melodic patterns in large collections of Indian art music.
The approach first discovers melodic patterns within an audio
recording and subsequently searches for their repetitions in the
entire music collection. We compute similarity between melodic
patterns using dynamic time warping (DTW). Furthermore, we
investigate four different variants of the DTW cost function
for rank refinement of the obtained results. The music collection
used in this study comprises 1,764 audio recordings
with a total duration of 365 hours. Over 13 trillion DTW
distance computations are done for the entire dataset. Due
to the computational complexity of the task, different lower
bounding and early abandoning techniques are applied during
DTW distance computation. An evaluation based on expert
feedback on a subset of the dataset shows that the discovered
melodic patterns are musically relevant. Several musically
interesting relationships are discovered, yielding further scope
for establishing novel similarity measures based on melodic
patterns. The discovered melodic patterns can further be used
in challenging computational tasks such as automatic r¯aga
recognition, composition identification and music recommendation.
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