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Título:
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Particle filters for efficient meter tracking with dynamic bayesian networks
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Autor/a:
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Srinivasamurthy, Ajay; Holzapfel, Andre; Cemgil, Ali Taylan; Serra, Xavier
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Abstract:
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Comunicació presentada a la 16th International Society for Music Information Retrieval Conference (ISMIR 2015), celebrada els dies 26 a 30 d'octubre de 2015 a Màlaga, Espanya. |
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Abstract:
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Recent approaches in meter tracking have successfully applied
Bayesian models. While the proposed models can
be adapted to different musical styles, the applicability of
these flexible methods so far is limited because the application
of exact inference is computationally demanding.
More efficient approximate inference algorithms using particle
filters (PF) can be developed to overcome this limitation.
In this paper, we assume that the type of meter of a
piece is known, and use this knowledge to simplify an existing
Bayesian model with the goal of incorporating a more
diverse observation model. We then propose Particle Filter
based inference schemes for both the original model and
the simplification. We compare the results obtained from
exact and approximate inference in terms of meter tracking
accuracy as well as in terms of computational demands.
Evaluations are performed using corpora of Carnatic music
from India and a collection of Ballroom dances. We document
that the approximate methods perform similar to exact
inference, at a lower computational cost. Furthermore, we
show that the inference schemes remain accurate for long
and full length recordings in Carnatic music. |
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Abstract:
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This work is supported by the European Research Council
(grant number 267583) and a Marie Curie Intra-European
Fellowship (grant number 328379). |
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Derechos:
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© Ajay Srinivasamurthy, Andre Holzapfel, Ali Taylan
Cemgil, Xavier Serra.
Licensed under a Creative Commons Attribution 4.0 International License
(CC BY 4.0). Attribution: Ajay Srinivasamurthy, Andre Holzapfel,
Ali Taylan Cemgil, Xavier Serra. “Particle Filters for Efficient Meter
Tracking with Dynamic Bayesian Networks”, 16th International Society
for Music Information Retrieval Conference, 2015.
https://creativecommons.org/licenses/by/4.0/
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Tipo de documento:
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Objeto de conferencia
Artículo - Versión publicada |
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Editor:
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International Society for Music Information Retrieval (ISMIR)
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