Downbeat tracking using beat synchronous features with recurrent neural networks
|F. Krebs, S. Böck, M. Dorfer, G. Widmer. Downbeat tracking using beat synchronous features with recurrent neural networks. pages 129-135, 8, 2016.|
|Buch||Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016)|
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.