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Títol:
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Landmark detection in Hindustani music melodies
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Autor/a:
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Gulati, Sankalp; Serrà Julià, Joan; Ganguli, Kaustuv Kanti; Serra, Xavier
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Abstract:
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Comunicació presentada a la International Computer Music Conference, ICMC/SMC, celebrada a Atenes (Grècia) del 14 al 20 de setembre de 2014. |
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Abstract:
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Musical melodies contain hierarchically organized events,
where some events are more salient than others, acting
as melodic landmarks. In Hindustani music melodies, an
important landmark is the occurrence of a nyas. Occurrence
of nyas is crucial to build and sustain the format of
a r¯ag and mark the boundaries of melodic motifs. Detection
of ny¯as segments is relevant to tasks such as melody
segmentation, motif discovery and r¯ag recognition. However,
detection of ny¯as segments is challenging as these
segments do not follow explicit set of rules in terms of segment
length, contour characteristics, and melodic context.
In this paper we propose a method for the automatic detection
of nyas segments in Hindustani music melodies. It
consists of two main steps: a segmentation step that incorporates
domain knowledge in order to facilitate the placement
of nyas boundaries, and a segment classification step
that is based on a series of musically motivated pitch contour
features. The proposed method obtains significant accuracies
for a heterogeneous data set of 20 audio music
recordings containing 1257 nyas svar occurrences and total
duration of 1.5 hours. Further, we show that the proposed
segmentation strategy significantly improves over a
classical piece-wise linear segmentation approach. |
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Abstract:
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This work is partly supported by the European Research
Council under the European Union’s Seventh Framework
Program, as part of the CompMusic project (ERC grant
agreement 267583). J.S. acknowledges 2009-SGR-1434
from Generalitat de Catalunya, ICT-2011-8-318770 from
the European Commission, JAEDOC069/2010 from CSIC,
and European Social Funds. |
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Drets:
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© 2014 Sankalp Gulati et al. This is an open-access article distributed
under the terms of the Creative Commons Attribution 3.0 Unported License, which
permits unrestricted use, distribution, and reproduction in any medium, provided
the original author and source are credited
https://creativecommons.org/licenses/by/3.0/
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Tipus de document:
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Objecte de conferència
Article - Versió publicada |
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Publicat per:
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Michigan Publishing
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Compartir:
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