Work

Off the Beaten Track: Using Deep Learning to Interpolate Between Music Genres

Year: 2018

Type: preprint

Abstract: We describe a system based on deep learning that generates drum patterns in the electronic dance music domain. Experimental results reveal that generated patterns can be employed to produce musically sound and creative transitions between different genres, and that the process of generation is of interest to practitioners in the field.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.1804.09808

Authors: Borghuis, Tijn, Tibo, Alessandro, Conforti, Simone, Canciello, Luca, Brusci, Lorenzo, Frasconi, Paolo

Language: English

Topic: Music Technology and Sound Studies

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2799187451

Cites: 14

Cited by: 2

Related to: 10