Name
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.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3308557.3308673 | Proceedings of the 24th International Conference on Intelligent User Interfaces: Companion |
Keywords | Field | DocType |
deep learning, drum machine, drum pattern generation, generative adversarial networks | Electronic dance music,Loudness,Generative adversarial network,Computer science,Musical,Drum,Human–computer interaction,Artificial intelligence,Deep learning,Generative grammar,Artificial neural network | Conference |
ISBN | Citations | PageRank |
978-1-4503-6673-1 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Richard Vogl | 1 | 7 | 2.06 |
| Eghbal-zadeh Hamid | 2 | 41 | 9.28 |
| Peter Knees | 3 | 594 | 51.71 |