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2010
ACM

Improving automatic music classification performance by extracting features from different types of data

9 years 5 days ago
Improving automatic music classification performance by extracting features from different types of data
This paper discusses two sets of automatic musical genre classification experiments. Promising research directions are then proposed based on the results of these experiments. The first set of experiments was designed to examine the utility of combining features extracted from separate and independent audio, symbolic and cultural sources of musical information. The results from this set of experiments indicate that combining feature types can indeed substantively improve classification accuracy as well as reduce the seriousness of those misclassifications that do occur. The second set of experiments examined which high-level features were most important in successfully classifying symbolic data. It was found that features associated with instrumentation were particularly effective. The paper also presents the jMIR toolset, which was used to carry out these experiments and which is particularly well suited to combining information extracted from different types of data sources. jMIR is...
Cory McKay, Ichiro Fujinaga
Added 17 May 2010
Updated 17 May 2010
Type Conference
Year 2010
Where MIR
Authors Cory McKay, Ichiro Fujinaga
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