Talks and Poster Presentations (with Proceedings-Entry):

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.

English abstract:
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

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.