An experiment for the automatic detection of expressive- ness in music performances using a perceptive based audi- tory models is presented. We recognize the intentions with reference to the Kinematics Energy expressive space. Au- dio features have been firstly extracted using a perception- based analysis, then we have made several analyses on timing and spectral features over overlapping sliding win- dows, estimating average and variance for each one of the features. Using a naive Bayesian classifier we investigated which features are most relevant for expression detection. This experiment also yielded interesting contributions for tuning the Kinematics Energy space with new features.

Expressiveness detection of music performances in the Kinematics Energy Space

MION, LUCA;DE POLI, GIOVANNI
2004

Abstract

An experiment for the automatic detection of expressive- ness in music performances using a perceptive based audi- tory models is presented. We recognize the intentions with reference to the Kinematics Energy expressive space. Au- dio features have been firstly extracted using a perception- based analysis, then we have made several analyses on timing and spectral features over overlapping sliding win- dows, estimating average and variance for each one of the features. Using a naive Bayesian classifier we investigated which features are most relevant for expression detection. This experiment also yielded interesting contributions for tuning the Kinematics Energy space with new features.
Proc. Sound and Music Computing Conf. (JIM/CIM 04)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3190451
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