Chord Label Personalization through Deep Learning of Integrated Harmonic Interval-based Representations

Autor:	Koops, H. V., de Haas, W. B., Bransen, J., Volk, A.
Rok vydání:	2017
Předmět:	Computer Science - Sound Computer Science - Multimedia Computer Science - Neural and Evolutionary Computing 68Txx C.1.3 H.5.1
Zdroj:	Proc. of the Int. Workshop on Deep Learning and Music. Anchorage, US. 1(1). pp19-25 (2017)
Druh dokumentu:	Working Paper
Popis:	The increasing accuracy of automatic chord estimation systems, the availability of vast amounts of heterogeneous reference annotations, and insights from annotator subjectivity research make chord label personalization increasingly important. Nevertheless, automatic chord estimation systems are historically exclusively trained and evaluated on a single reference annotation. We introduce a first approach to automatic chord label personalization by modeling subjectivity through deep learning of a harmonic interval-based chord label representation. After integrating these representations from multiple annotators, we can accurately personalize chord labels for individual annotators from a single model and the annotators' chord label vocabulary. Furthermore, we show that chord personalization using multiple reference annotations outperforms using a single reference annotation. Comment: Proceedings of the First International Conference on Deep Learning and Music, Anchorage, US, May, 2017 (arXiv:1706.08675v1 [cs.NE])
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1706.09552 Zobrazit plný text záznamu View this record from Arxiv