Florian Krebs, Sebastian Böck, Matthias Dorfer, Gerhard Widmer,
"Downbeat Tracking Using Beat-synchronous Features and Recurrent Neural Networks"
: Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR)., 8-2016
Original Titel:
Downbeat Tracking Using Beat-synchronous Features and Recurrent Neural Networks
Sprache des Titels:
Englisch
Original Buchtitel:
Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR).
Original Kurzfassung:
In this paper, we propose a system that extracts the downbeat
times from a beat-synchronous audio feature stream
of a music piece. Two recurrent neural networks are used
as a front-end: the first one models rhythmic content on
multiple frequency bands, while the second one models the
harmonic content of the signal. The output activations are
then combined and fed into a dynamic Bayesian network
which acts as a rhythmical language model. We show on
seven commonly used datasets of Western music that the
system is able to achieve state-of-the-art results.
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