On Efficient Music Genre Classification
Jialie Shen,
John Shepherd,
Anne H.H. Ngu
Database Systems for Advanced Applications:
10th International Conference, DASFAA 2005,
Beijing, China, April 17-20, 2005.
Automatic music genre classification has long been an important
problem. However, there is a paucity of literature that addresses
the issue, and in addition, reported accuracy is fairly low. In this
paper, we present empirical study of a novel music descriptor generation
method for efficient content based music genre classification. Analysis
and empirical evidence demonstrate that our approach outperforms
state-of-the-art approaches in the areas including accuracy of genre
classification with various machine learning algorithms, efficiency
on training process. Furthermore, its effectiveness is robust against
various kinds of audio alternation.
Keys:
Music Classification, Genre, Human Factors.
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