Paper year
2023
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Application of Computational Methods for Comparative Music Analysis
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Abstract
Music performance analysis can thrive from computational methods of music information retrieval. Besides extracting and analyzing symbolic music data, performance analysis also focuses on retrieving performance parameters from digital audio recordings. On the other hand, the aim of the comparative performance analysis is often qualitative and stands on our perception and musical principles. In this paper, we utilize feature extraction strategies and comparative analysis, leveraging computational methods while focusing on the goals of musicology. We aim to provide insight into music performance data for subsequent case studies. As the main contribution of this paper, we present a specific combination of extraction methods for performance music analysis on the application level. Furthermore, we demonstrate an early version of open-source software that deploys the proposed strategy in a user-friendly web-based environment.
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