A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Aggregate features and ADABOOST for music classification
Machine Learning
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probing the Pareto Frontier for Basis Pursuit Solutions
SIAM Journal on Scientific Computing
MULTIBOOST: a multi-purpose boosting package
The Journal of Machine Learning Research
An analysis of the GTZAN music genre dataset
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Proceedings of the 20th ACM international conference on Multimedia
Classification accuracy is not enough
Journal of Intelligent Information Systems
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We re-implement two state-of-the-art systems for music genre recognition, and closely examine their behavior. First, we find specific excerpts each system consistently and persistently mislabels. Second, we test the robustness of each system to spectral adjustments to audio signals. Finally, we expose the internal genre models of each system by testing if human can recognize the genres of music excerpts composed by each system to be highly genre-representative. Our results suggest that, though they have high mean classification accuracies, neither system is recognizing music genre.