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Multi-Objective Evaluation of Music Classification

  • Igor Vatolkin
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Music classification targets the management of personal music collections or recommendation of new songs. Several steps are required here: feature extraction and processing, selection of the most relevant of them, and training of classification models. The complete classification chain is evaluated by a selected performance measure. Often standard confusion matrix based metrics like accuracy are calculated. However it can be valuable to compare the methods using further metrics depending on the current application scenario. For this work we created a large empirical study for different music categories using several feature sets, processing methods and classification algorithms. The correlation between different metrics is discussed, and the ideas for better algorithm evaluation are outlined.

Keywords

Mean Square Error Random Forest Music Information Retrieval Music Track Hard Category 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We thank the Klaus Tschira Foundation for financial support.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.TU DortmundDortmundGermany

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