Study and Application of Monte Carlo Algorithm for AI-Based Music Generation

DOI

10.2991/978-94-6463-012-1_44How to use a DOI?

Keywords

Music Generation; Monte Carlo Method; Data Analysis; MIDI

Abstract

When generating music via algorithms, it is essential to extract music characteristics and the distribution of notes. A Monte Carlo simulation framework for music generation was proposed on the premise of maintaining the authenticity of music samples. Those samples, which are stored in MIDI format, were first converted into data that can be processed by computes. To simulate music time series, we adopted Logistic regression. Except for time series, the converted data also include three parameters: duration, pitch, and velocity. We first solved the correlation coefficient matrix and standard deviation of the three parameters, and then analyzed them using Monte Carlo method and summarized their distribution patterns.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jun Min
AU  - Lei Wang
PY  - 2022
DA  - 2022/12/09
TI  - Study and Application of Monte Carlo Algorithm for AI-Based Music Generation
BT  - Proceedings of the 2022 International Conference on Educational Innovation and Multimedia Technology (EIMT 2022)
PB  - Atlantis Press
SP  - 392
EP  - 402
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-012-1_44
DO  - 10.2991/978-94-6463-012-1_44
ID  - Min2022
ER  -