Medley2K: A Dataset of Medley Transitions

Autor:	Faber, Lukas, Luck, Sandro, Pascual, Damian, Roth, Andreas, Brunner, Gino, Wattenhofer, Roger
Rok vydání:	2020
Předmět:	Computer Science - Sound Computer Science - Machine Learning Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	The automatic generation of medleys, i.e., musical pieces formed by different songs concatenated via smooth transitions, is not well studied in the current literature. To facilitate research on this topic, we make available a dataset called Medley2K that consists of 2,000 medleys and 7,712 labeled transitions. Our dataset features a rich variety of song transitions across different music genres. We provide a detailed description of this dataset and validate it by training a state-of-the-art generative model in the task of generating transitions between songs. Comment: MML 2020 - 13th Int. Workshop on Machine Learning and Music at ECML-PKDD 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2008.11159 Zobrazit plný text záznamu View this record from Arxiv