Florian Krebs, Sebastian Böck, Gerhard Widmer,
"An Efficient State-Space Model for Joint Tempo and Meter Tracking"
: Proceedings of the 16th International Society for Music Information Retrieval Conference, 10-2015
Original Titel:
An Efficient State-Space Model for Joint Tempo and Meter Tracking
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 16th International Society for Music Information Retrieval Conference
Original Kurzfassung:
Dynamic Bayesian networks (e.g., Hidden Markov Models)
are popular frameworks for meter tracking in music
because they are able to incorporate prior knowledge about
the dynamics of rhythmic parameters (tempo, meter, rhythmic
patterns, etc.). One popular example is the bar pointer
model, which enables joint inference of these rhythmic parameters
from a piece of music. While this allows the
mutual dependencies between these parameters to be exploited,
it also increases the computational complexity of
the models. In this paper, we propose a new state-space
discretisation and tempo transition model for this class of
models that can act as a drop-in replacement and not only
increases the beat and downbeat tracking accuracy, but also
reduces time and memory complexity drastically. We incorporate
the new model into two state-of-the-art beat and
meter tracking systems, and demonstrate its superiority to
the original models on six datasets.
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