pith. sign in

arxiv: 1706.09558 · v1 · pith:IR7TW3XOnew · submitted 2017-06-29 · 💻 cs.SD · cs.MM· cs.NE

Talking Drums: Generating drum grooves with neural networks

classification 💻 cs.SD cs.MMcs.NE
keywords drumsequencefoundgeneratingneuraloutputsamplingadopted
0
0 comments X
read the original abstract

Presented is a method of generating a full drum kit part for a provided kick-drum sequence. A sequence to sequence neural network model used in natural language translation was adopted to encode multiple musical styles and an online survey was developed to test different techniques for sampling the output of the softmax function. The strongest results were found using a sampling technique that drew from the three most probable outputs at each subdivision of the drum pattern but the consistency of output was found to be heavily dependent on style.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Adaptive Music Composition for Games

    cs.MM 2019-07 unverdicted novelty 5.0

    An adaptive music system combining cognitive models with multi-agent composition was integrated into two games and produced higher reported player immersion and music-game correlation than the original soundtracks.