Towards an Accessible and Rapidly Trainable Rhythm Sequencer Using a Generative Stacked Autoencoder

Autor:	Wastnidge, Alex
Rok vydání:	2024
Předmět:	Computer Science - Sound Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: 7 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.07034 Zobrazit plný text záznamu View this record from Arxiv