Name
Title | ||
|---|---|---|
Estimation of the Kansei Information obtained from Musical Scores via Machine Learning Algorithms : - Classification of Tempo into Two Classes Using Only Information Available in Musical Scores - |
Abstract | ||
|---|---|---|
This study investigates whether machine learning algorithms can be used to accurately classify tempo into two classes based only on the musical note sequence written on musical scores. Herein, the tempo that is manually estimated by looking at the score is simulated via Kansei (emotional) information processing. The tempo threshold was set at d = 120. Results showed that even after successful learning, the algorithms showed low recognition rates while classifying slow tempo class from the evaluation data and some data were erroneously recognized. In contrast, the algorithms showed high recognition rates when classifying fast tempo class from the evaluation data. The algorithms did not show any recognition error in the data. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICAwST.2019.8923480 | 2019 IEEE 10th International Conference on Awareness Science and Technology (iCAST) |
Keywords | Field | DocType |
Kansei (emotional) information,machine learning,musical score,tempo,classification | Information processing,Musical,Computer science,Algorithm,Kansei,Artificial intelligence,Musical note,Machine learning | Conference |
ISSN | ISBN | Citations |
2325-5986 | 978-1-7281-3822-0 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Satoshi Kawamura | 1 | 0 | 0.34 |
| Zhongda Liu | 2 | 0 | 0.68 |
| Hitoaki Yoshida | 3 | 0 | 2.70 |