Towards an Accessible and Rapidly Trainable Rhythm Sequencer Using a Generative Stacked Autoencoder
Year: 2024
Type: preprint
Abstract: Neural networks and deep learning are often deployed for the sake of the most comprehensive music generation with as little involvement as possible from the human musician. Implementations in aid of, or being a tool for, music practitioners are sparse. This paper proposes the integration of generative stacked autoencoder structures for rhythm generation, within a conventional melodic step-sequencer. It further aims to work towards its implementation being accessible to the average electronic music practitioner. Several model architectures have been trained and tested for their creative potential. While the currently implementations do display limitations, they do represent viable creative solutions for music practitioners.
Source: arXiv (Cornell University)
Author: Wastnidge, Alex
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4396913269