# musicaiz: A Python Library for Symbolic Music Generation, Analysis and Visualization

arXiv: https://arxiv.org/abs/2209.07974 · HTML: https://mlsanity.com/paper/2209.07974/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2022.

## Built on by (most cited first, 3 of 3)

- [End-to-End Full-Page Optical Music Recognition for Pianoform Sheet Music](https://mlsanity.com/paper/2405.12105/) (2024)
- [A Survey on Artificial Intelligence for Music Generation: Agents, Domains and Perspectives](https://mlsanity.com/paper/2210.13944/) (2022)
- [Symbolic Music Structure Analysis with Graph Representations and Changepoint Detection Methods](https://mlsanity.com/paper/2303.13881/) (2023)

## 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.