Sebastian Böck, Florian Krebs, Gerhard Widmer,
"Joint Beat and Downbeat Tracking with Recurrent Neural Networks"
: Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR), 8-2016
Original Titel:
Joint Beat and Downbeat Tracking with Recurrent Neural Networks
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR)
Original Kurzfassung:
JOINT BEAT AND DOWNBEAT TRACKING WITH RECURRENT
NEURAL NETWORKS
Sebastian B
?
ock, Florian Krebs, and Gerhard Widmer
Department of Computational Perception
Johannes Kepler University Linz, Austria
sebastian.boeck@jku.at
ABSTRACT
In this paper we present a novel method for jointly extract-
ing beats and downbeats from audio signals. A recurrent
neural network operating directly on magnitude spectro-
grams is used to model the metrical structure of the audio
signals at multiple levels and provides an output feature
that clearly distinguishes between beats and downbeats.
A dynamic Bayesian network is then used to model bars
of variable length and align the predicted beat and down-
beat positions to the global best solution. We find that the
proposed model achieves state-of-the-art performance on a
wide range of different musical genres and styles.