musicaiz: A Python Library for Symbolic Music Generation, Analysis and Visualization
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Abstract (arXiv)
In this article, we present musicaiz, an object-oriented library for analyzing, generating and evaluating symbolic music. The submodules of the package allow the user to create symbolic music data from scratch, build algorithms to analyze symbolic music, encode MIDI data as tokens to train deep learning sequence models, modify existing music data and evaluate music generation systems. The evaluation submodule builds on previous work to objectively measure music generation systems and to be able to reproduce the results of music generation models. The library is publicly available online. We encourage the community to contribute and provide feedback.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Sep 2022.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.
3 papers.
- 2024End-to-End Full-Page Optical Music Recognition for Pianoform Sheet Music20 May 2024
- 2023Symbolic Music Structure Analysis with Graph Representations and Changepoint Detection Methods24 Mar 2023
- 2022A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives25 Oct 2022