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R. Vogl, H. Eghbal-Zadeh, P. Knees:
"An Automatic Drum Machine with Touch UI Based on a Generative Neural Network";
Poster: 24th International Conference on Intelligent User Interfaces, Marina del Rey, CA, USA; 2019-03-16 - 2019-03-20; in: "Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion", ACM, New York, NY, USA (2019), ISBN: 9781450366731; 91 - 92.

Drum machines are an important tool for music production in the context of electronic dance music. In this work we introduce a drum machine which automatically generates drum patterns according to the high-level stylistic cues of musical genre, complexity, and loudness, controlled by the user. In comparable tools, usually a predefined collection of drum patterns serves as the source for suggestions. In order to yield a greater variety of patterns and to create original patterns, we suggest the use of stochastic generative models. Therefore, in this work, drum patterns are generated using a generative adversarial network, trained on a large-scale drum pattern library. As a method to enter, edit, visualize, and generate patterns, a touch-based step sequencer interface is augmented with controls of the semantic dimensions of genre, complexity, and loudness.

Drum pattern generation, generative adversarial networks, drum machine, deep learning

http://dx.doi.org/10.1145/3308557.3308673

https://doi.org/10.1145/3308557.3308673