Everybody Compose: Deep Beats To Music

This project presents a deep learning approach to generate monophonic melodies based on input beats, allowing even amateurs to create their own music compositions. Three effective methods - LSTM with Full Attention, LSTM with Local Attention, and Transformer with Relative Position Representation - a...

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Bibliographic Details
Published inarXiv.org
Main Authors Shen, Conghao, Yao, Violet Z, Liu, Yixin
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 09.06.2023
Subjects
Composition effects
Computer Science - Learning
Computer Science - Multimedia
Computer Science - Sound
Keyboards
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Summary:This project presents a deep learning approach to generate monophonic melodies based on input beats, allowing even amateurs to create their own music compositions. Three effective methods - LSTM with Full Attention, LSTM with Local Attention, and Transformer with Relative Position Representation - are proposed for this novel task, providing great variation, harmony, and structure in the generated music. This project allows anyone to compose their own music by tapping their keyboards or ``recoloring'' beat sequences from existing works.
ISSN:2331-8422
DOI:10.48550/arxiv.2306.06284