Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Content-based organization and visualization of music archives
Proceedings of the tenth ACM international conference on Multimedia
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
Combination of audio and lyrics features for genre classification in digital audio collections
MM '08 Proceedings of the 16th ACM international conference on Multimedia
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Current research on the task of automatic music genre classification has been focusing on new classification approaches based on combining information from other sources than the music signal. The reason for this is that the use of content-based approaches, i.e. using features extracted directly from the audio signal, seems to have reached a glass ceiling. In this work we show that by using different types of content-based features together it is possible to substantially improve the classification accuracy. This is an interesting result as different types of content-based features aim, at a conceptual level, to capture the same type of information. In order to identify which types of content-based features are responsible for the predictive accuracy gain, we also used a feature selection (FS) approach based on a genetic algorithm (GA). The analysis of the results in two databases shows that the use of the GA for FS succeeds in selecting a representative subset without significant loss in accuracy. It also shows that all the different types of content-based features employed are important for the improvement of the accuracy in classifying music genres.