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A method for transferring the expressive musical nuances of real recordings to a MIDI synthesized version was successfully demonstrated. Three features (dynamics, tempo, and articulation) were extracted from the recordings and then applied to the MIDI note list in order to reproduce the performer’s style. Subjective results showed that the retargeted music is very natural and sounds similar to the original performance. Statistical tests confirmed that the output correlated with the original better than with other sources. The method can successfully distinguish among different styles. A variety of applications can use this approach.
Author (s): Lui, Simon; Horner, Andrew; So, Clifford
Affiliation:
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong; School of Continuing and Professional Studies, Chinese University of Hong Kong, Hong Kong
(See document for exact affiliation information.)
Publication Date:
2010-12-06
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Permalink: https://aes2.org/publications/elibrary-page/?id=15744
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Lui, Simon; Horner, Andrew; So, Clifford; 2010; Retargeting Expressive Musical Style from Classical Music Recordings Using a Support Vector Machine [PDF]; Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong; School of Continuing and Professional Studies, Chinese University of Hong Kong, Hong Kong; Paper ; Available from: https://aes2.org/publications/elibrary-page/?id=15744
Lui, Simon; Horner, Andrew; So, Clifford; Retargeting Expressive Musical Style from Classical Music Recordings Using a Support Vector Machine [PDF]; Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong; School of Continuing and Professional Studies, Chinese University of Hong Kong, Hong Kong; Paper ; 2010 Available: https://aes2.org/publications/elibrary-page/?id=15744
@article{lui2010retargeting,
author={lui simon and horner andrew and so clifford},
journal={journal of the audio engineering society},
title={retargeting expressive musical style from classical music recordings using a support vector machine},
year={2010},
volume={58},
issue={12},
pages={1032-1044},
month={february},}