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 -

Satoshi Kawamura, Zhongda Liu, H. Yoshida
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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 ♩ = 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.
通过机器学习算法估计从乐谱中获得的感性信息:-仅使用乐谱中可用的信息将节奏分为两类-
本研究探讨了机器学习算法是否可以仅根据乐谱上的音符顺序将节奏准确地分为两类。在这里,通过观察乐谱手动估计的节奏是通过感性(情感)信息处理模拟的。速度阈值设定为“♩= 120”。结果表明,即使在学习成功后,该算法在从评价数据中对慢节奏类进行分类时也表现出较低的识别率,部分数据被错误识别。相比之下,该算法在从评价数据中对快节奏类进行分类时显示出较高的识别率。该算法在数据中没有显示出任何识别错误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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