A Machine Learning Approach for Analyzing Musical Expressions of Piano Performance

摘要：

This paper proposed a machine learning approach for analyzing teachers’ expert knowledge of classifying students’ piano performance into approximate expression categories. Students are usually confused when learning the expressive performance because of teachers’ subjective intention difference on the same performance. In this paper, teacher models will be built by analyzing teachers’ classification rules. By replaying their performances and read teachers’ suggestions in graphical and textual modes which are generated automatically by teacher model, students could understand the nuance of performance features on each expression. Three teachers and ten students joined this experiment. Sixty piano performances were recorded for constructing the teacher models. The average accuracy of teacher models for classifying performance expression is 70.8％. Questionnaires reflect both teachers and students are satisfied with the user interface, generated suggestions, and classification rules.

作者: Kuo-Liang Ou Pao-Te Tsai Wern-huar Tarng

作者单位: Graduate Instute of Computer Science, National Hsin-Chu Uinversity of Education

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-5

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）