Downbeat Tracking Using Beat-synchronous Features and Recurrent Neural Networks
Sprache des Titels:
Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR).
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.