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

2018/04/25 by Tijn Borghuis, Borghuis, Tijn, Alessandro Tibo +9
Computer Science · Engineering · #Audio and Speech Processing (eess.AS) #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Multimedia (cs.MM) #Music Technology and Sound Studies #Music and Audio Processing #Sound (cs.SD) #cs.LG #cs.MM #cs.SD #eess.AS #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1804.09808

openalex publication_date 2018/04/25 · arxiv created 2018/05/02 · arxiv updated 2018/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Auto-Encoding Variational Bayes
• CAN: Creative Adversarial Networks, Generating "Art" by Learning About\n Styles and Deviating from Style Norms
• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

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