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 ...
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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