Off the Beaten Track: Using Deep Learning to Interpolate Between Music Genres
arXiv:1804.09808v2
AI Analysis
This addresses the need for automated music generation tools for electronic dance music producers, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackled the problem of generating drum patterns for electronic dance music using deep learning, resulting in musically sound and creative transitions between genres that are of interest to practitioners.
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.