The Beauty of Repetition: An Algorithmic Composition Model With Motif-Level Repetition Generator and Outline-to-Music Generator in Symbolic Music Generation

Most musical compositions utilize repetition as a fundamental element to create captivating aesthetic experiences. However, the potential of repetition in machine-learning-based algorithmic composition has not been thoroughly investigated. This article aims to make an initial attempt at repetition m...

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Bibliographic Details
Published in:IEEE transactions on multimedia 2024, Vol.26, p.4320-4333
Main Authors: Hu, Zhejing, Ma, Xiao, Liu, Yan, Chen, Gong, Liu, Yongxu, Dannenberg, Roger B.
Format: Article
Language:English
Subjects:
Algorithmic composition
Algorithms
Composition
Computational modeling
Generators
Machine learning
Machine learning algorithms
Multimedia computing
Music
Repetition
repetition modeling
Rhythm
symbolic music generation
Transformers
Citations: Items that this one cites
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Summary:Most musical compositions utilize repetition as a fundamental element to create captivating aesthetic experiences. However, the potential of repetition in machine-learning-based algorithmic composition has not been thoroughly investigated. This article aims to make an initial attempt at repetition modeling by generating motif-level repetitions and integrating them into music through a combination of example-based and domain knowledge-based learning techniques. The article presents a new Motif-to-music Generation Model (MGM) that combines a motif-level repetition generator (MRG) and an outline-to-music generator (O2MG). To train this model, a new music repetition dataset (MRD) has been created, which includes 584,329 samples from various categories of motif repetition and 3,545 outline-music sequences from pop piano music. The MRG uses a Transformer encoder to learn the representation of music notes from MRD, while the repetition-aware learner in MRG takes advantage of the unique characteristics of repetitions based on music theory. The O2MG applies a novel outline-to-music learning strategy to learn the relationships among motif-level repetitions in the music and generate music based on these repetitions. The experiments show that MGM can generate a variety of beautiful repetitions with any given motif, improving the music quality and structure of machine-composed music.
ISSN:1520-9210
1941-0077
DOI:10.1109/TMM.2023.3321495