Downbeat tracking using beat synchronous features with recurrent neural networks

Autoren Florian Krebs
Sebastian Böck
Matthias Dorfer
Gerhard Widmer
Editoren
TitelDownbeat tracking using beat synchronous features with recurrent neural networks
BuchtitelProceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016)
Typin Konferenzband
MonatAugust
Jahr2016
Seiten129-135
SCCH ID#16099
Abstract

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.