IEEE Transactions on Pattern Analysis and Machine Intelligence
Manipulation, analysis and retrieval systems for audio signals
Manipulation, analysis and retrieval systems for audio signals
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Aggregate features and ADABOOST for music classification
Machine Learning
Combination of audio and lyrics features for genre classification in digital audio collections
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Genre classification using chords and stochastic language models
Connection Science - Music, Brain, Cognition
Comparison of classifier fusion methods for classification in pattern recognition tasks
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Pattern Recognition Approach for Music Style Identification Using Shallow Statistical Descriptors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Music genre classification using explicit semantic analysis
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
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In this paper, we evaluate the impact of feature selection on the classification accuracy and the achieved dimensionality reduction, which benefits the time needed on training classification models. Our classification scheme therein is a Cartesian ensemble classification system, based on the principle of late fusion and feature subspaces. These feature subspaces describe different aspects of the same data set. We use it for the ensemble classification of multiple feature sets from the audio and symbolic domains. We present an extensive set of experiments in the context of music genre classification, based on Music IR benchmark datasets. We show that while feature selection does not benefit classification accuracy, it greatly reduces the dimensionality of each feature subspace, and thus adds to great gains in the time needed to train the individual classification models that form the ensemble.