@inproceedings{2c1acfcd468a4fa4a5e36e99ca33d5d6,
title = "A comparison of melody created by artificial intelligence and human based on mathematical model",
abstract = "There have been many achievements in using Artificial Intelligence (AI) to automatically compose melody. But there still need statistical methods to evaluate the degree of similarity between AI-generated melody and human composed melody. With N-gram models, this research compared the distribution of transitions of a combination of rhythm and pitch of melodies between melodies generated by AI (Magenta of Google) and the original British and American folk songs pieces used for training. The result shows that the AI system is good at modeling the common patterns of note transitions which appear more frequently in the data set of human-composed melodies than humans, and such patterns appear more frequently in the generated melodies compared with human-composed melody. The experiment shows that using the N-gram model to analyze the distribution characteristics of pitch and rhythm features of melody to distinguish between the AI-generated melody dataset and human composed melody dataset is a direction worthy of further research.",
keywords = "Melody, N-gram, Pitch, Rhythm, Similarity",
author = "Ziming Li and Shengchen Li",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd 2020.; 7th Conference on Sound and Music Technology, CSMT 2019 ; Conference date: 26-12-2019 Through 29-12-2019",
year = "2020",
doi = "10.1007/978-981-15-2756-2_10",
language = "English",
isbn = "9789811527555",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer",
pages = "121--130",
editor = "Haifeng Li and Lin Ma and Shengchen Li and Chunying Fang and Yidan Zhu",
booktitle = "Proceedings of the 7th Conference on Sound and Music Technology CSMT 2019, Revised Selected Papers",
}