The Strength of Weak Learnability
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Machine Learning
Pairwise Classification as an Ensemble Technique
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Nearest Neighbors in Random Subspaces
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A comparative study on content-based music genre classification
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
MARSYAS: a framework for audio analysis
Organised Sound
MARSYAS: a framework for audio analysis
Organised Sound
MIR '03 Proceedings of the 5th ACM SIGMM international workshop on Multimedia information retrieval
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Adaptive mixtures of local experts
Neural Computation
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In this paper we propose a novel time–space ensemble–based approach for the task of automatic music genre classification. Ensemble strategies employ several classifiers to different views of the problem–space, and combination rules in order to produce the final classification decision. In our approach we employ audio signal segmentation in time intervals and also problem space decomposition. Initially the music signal is split in time segments; features are extracted from these music signal segments and the one against all (OAA) and round robin (RR) strategies, which implement a space decomposition by using several binary classifiers, are applied. Finally, the outputs of the set of classifiers are combined to produce the final result. We test our proposition in a music database of 1.200 music samples from four different music genres. Experimental results show that time segment decomposition is more important than the space decomposition produced by the OAA and RR strategies, although they produce better results relative to the use of single classifiers and feature vectors.