A GAN Based Drum Pattern Generation UI Prototype

Hamid Eghbal-Zadeh; Richard Vogl; Gerhard Widmer; Peter Knees

A GAN Based Drum Pattern Generation UI Prototype

Hamid Eghbal-Zadeh, Richard Vogl, Gerhard Widmer, Peter Knees

Institute of Computational Perception

Research output: Chapter in Book/Report/Conference proceeding › Conference proceedings › peer-review

Abstract

Tools which support artists with the generation of drum tracks for music productions have gained popularity in recent years. Especially when utilizing digital audio workstations, using e.g. predefined drum loops can speed up drafting a musical idea. However, regarding variation and originality of drum patterns, methods based on generative models would be more suited for this task. In this work we present a drum pattern generation prototype based on a step sequencer interface which allows to explore the suitability of generative adversarial networks for this task.

Original language	English
Title of host publication	Proceedings of ISMIR2018 Late Breaking Demos
Number of pages	2
Publication status	Published - Sept 2018

Fields of science

202002 Audiovisual media
102 Computer Sciences
102001 Artificial intelligence
102003 Image processing
102015 Information systems

JKU Focus areas

Computation in Informatics and Mathematics
Engineering and Natural Sciences (in general)

Cite this

@inproceedings{a116e79f5c924e0f85ec3d5a053a20b1,

title = "A GAN Based Drum Pattern Generation UI Prototype",

abstract = "Tools which support artists with the generation of drum tracks for music productions have gained popularity in recent years. Especially when utilizing digital audio workstations, using e.g. predefined drum loops can speed up drafting a musical idea. However, regarding variation and originality of drum patterns, methods based on generative models would be more suited for this task. In this work we present a drum pattern generation prototype based on a step sequencer interface which allows to explore the suitability of generative adversarial networks for this task.",

author = "Hamid Eghbal-Zadeh and Richard Vogl and Gerhard Widmer and Peter Knees",

year = "2018",

month = sep,

language = "English",

booktitle = "Proceedings of ISMIR2018 Late Breaking Demos",

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TY - GEN

T1 - A GAN Based Drum Pattern Generation UI Prototype

AU - Eghbal-Zadeh, Hamid

AU - Vogl, Richard

AU - Widmer, Gerhard

AU - Knees, Peter

PY - 2018/9

Y1 - 2018/9

N2 - Tools which support artists with the generation of drum tracks for music productions have gained popularity in recent years. Especially when utilizing digital audio workstations, using e.g. predefined drum loops can speed up drafting a musical idea. However, regarding variation and originality of drum patterns, methods based on generative models would be more suited for this task. In this work we present a drum pattern generation prototype based on a step sequencer interface which allows to explore the suitability of generative adversarial networks for this task.

AB - Tools which support artists with the generation of drum tracks for music productions have gained popularity in recent years. Especially when utilizing digital audio workstations, using e.g. predefined drum loops can speed up drafting a musical idea. However, regarding variation and originality of drum patterns, methods based on generative models would be more suited for this task. In this work we present a drum pattern generation prototype based on a step sequencer interface which allows to explore the suitability of generative adversarial networks for this task.

UR - https://drive.google.com/file/d/12uktQ77d7MNyuQdOUfNKrT6zNIaJ-HeY/view

M3 - Conference proceedings

BT - Proceedings of ISMIR2018 Late Breaking Demos

ER -

A GAN Based Drum Pattern Generation UI Prototype

Abstract

Fields of science

JKU Focus areas

Other files and links

Strategic FExFE Project on Deep Learning

Cite this

A GAN Based Drum Pattern Generation UI Prototype

Abstract

Fields of science

JKU Focus areas

Other files and links

Projects

Strategic FExFE Project on Deep Learning

Cite this