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
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
Music artist style identification by semi-supervised learning from both lyrics and content
Proceedings of the 12th annual ACM international conference on Multimedia
Fast Recognition of Musical Genres Using RBF Networks
IEEE Transactions on Knowledge and Data Engineering
A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures
Computer Music Journal
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Aggregate features and ADABOOST for music classification
Machine Learning
Automatic genre classification of musical signals
EURASIP Journal on Applied Signal Processing
TWO GRAMMATICAL INFERENCE APPLICATIONS IN MUSIC PROCESSING
Applied Artificial Intelligence
Pattern Recognition, Fourth Edition
Pattern Recognition, Fourth Edition
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Music Genre Classification Using a Hierarchical Clustering and a Language Model Approach
MMEDIA '09 Proceedings of the 2009 First International Conference on Advances in Multimedia
Probing the Pareto Frontier for Basis Pursuit Solutions
SIAM Journal on Scientific Computing
Content-based music genre classification using timbral feature vectors and support vector machine
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Semantic Gap?? Schemantic Schmap!! Methodological Considerations in the Scientific Study of Music
ISM '09 Proceedings of the 2009 11th IEEE International Symposium on Multimedia
IEEE Transactions on Audio, Speech, and Language Processing
Non-negative tensor factorization applied to music genre classification
IEEE Transactions on Audio, Speech, and Language Processing
Selection of Training Instances for Music Genre Classification
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Visualized Feature Fusion and Style Evaluation for Musical Genre Analysis
PCSPA '10 Proceedings of the 2010 First International Conference on Pervasive Computing, Signal Processing and Applications
Enhancing multi-label music genre classification through ensemble techniques
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Probabilistic and logic-based modelling of harmony
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
What is a "Musical World"? An affinity propagation approach
MIRUM '11 Proceedings of the 1st international ACM workshop on Music information retrieval with user-centered and multimodal strategies
Music genre classification using a time-delay neural network
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
AMR'10 Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion
MULTIBOOST: a multi-purpose boosting package
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Musical Genre Classification Using Nonnegative Matrix Factorization-Based Features
IEEE Transactions on Audio, Speech, and Language Processing
Multigroup classification of audio signals using time-frequency parameters
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A Survey of Audio-Based Music Classification and Annotation
IEEE Transactions on Multimedia
Genre classification for million song dataset using confidence-based classifiers combination
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Combining Visual and Acoustic Features for Music Genre Classification
ICMLA '11 Proceedings of the 2011 10th International Conference on Machine Learning and Applications and Workshops - Volume 02
A Novel Automatic Hierachical Approach to Music Genre Classification
ICMEW '12 Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops
Two systems for automatic music genre recognition: what are they really recognizing?
Proceedings of the second international ACM workshop on Music information retrieval with user-centered and multimodal strategies
An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics
An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics
Local and global scaling reduce hubs in space
The Journal of Machine Learning Research
Seven problems that keep MIR from attracting the interest of cognition and neuroscience
Journal of Intelligent Information Systems
Feature learning and deep architectures: new directions for music informatics
Journal of Intelligent Information Systems
The neglected user in music information retrieval research
Journal of Intelligent Information Systems
Evaluation in Music Information Retrieval
Journal of Intelligent Information Systems
Seven problems that keep MIR from attracting the interest of cognition and neuroscience
Journal of Intelligent Information Systems
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We argue that an evaluation of system behavior at the level of the music is required to usefully address the fundamental problems of music genre recognition (MGR), and indeed other tasks of music information retrieval, such as autotagging. A recent review of works in MGR since 1995 shows that most (82 %) measure the capacity of a system to recognize genre by its classification accuracy. After reviewing evaluation in MGR, we show that neither classification accuracy, nor recall and precision, nor confusion tables, necessarily reflect the capacity of a system to recognize genre in musical signals. Hence, such figures of merit cannot be used to reliably rank, promote or discount the genre recognition performance of MGR systems if genre recognition (rather than identification by irrelevant confounding factors) is the objective. This motivates the development of a richer experimental toolbox for evaluating any system designed to intelligently extract information from music signals.