Abstract
This study analyzes music using a stochastic process, particularly, the Vasicek model. This approach interprets note progression in a song as a mean-reverting process, allowing the estimation of three parameters such as the speed of the revision to the mean, long-term level of the mean, and volatility. In addition, the entropy is evaluated for each song to identify the randomness of rise-fall patterns for each music genre. Our results characterize certain types of music and could be used to create new indicators for music classification.
| Original language | English |
|---|---|
| Article number | 012010 |
| Journal | Journal of Physics: Conference Series |
| Volume | 2287 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2022 |
| Event | 2022 12th International Conference on Applied Physics and Mathematics, ICAPM 2022 - Singapore, Virtual, Singapore Duration: 2022 Feb 18 → 2022 Feb 20 |
Bibliographical note
Funding Information:This work was supported by (i) the Technology Innovation Program ATC+ (20014125, Development of Intelligent Management Solution for Nuclear Decommissioning Site Characterization) funded by the Ministry of Trade, Industry & Energy (MOTIE, Republic of Korea) and (ii) the Future-leading Research Initiative (Grant Number: 2021-22-0306) funded by Yonsei University.
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
All Science Journal Classification (ASJC) codes
- Physics and Astronomy(all)