Machine Learning
Machine Learning
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
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Digital Signal Processing
Robustness in Automatic Speech Recognition: Fundamentals and Applications
Robustness in Automatic Speech Recognition: Fundamentals and Applications
Speech and Audio Signal Processing: Processing and Perception of Speech and Music
Speech and Audio Signal Processing: Processing and Perception of Speech and Music
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
Increasing the Robustness of Boosting Algorithms within the Linear-programming Framework
Journal of VLSI Signal Processing Systems
A hybrid social-acoustic recommendation system for popular music
Proceedings of the 2007 ACM conference on Recommender systems
IEEE Transactions on Multimedia
A novel approach to musical genre classification using probabilistic latent semantic analysis model
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Audio signal representations for indexing in the transform domain
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Incorporating cultural representations of features into audio music similarity estimation
IEEE Transactions on Audio, Speech, and Language Processing
A learning approach to hierarchical feature selection and aggregation for audio classification
Pattern Recognition Letters
Feature selection in a cartesian ensemble of feature subspace classifiers for music categorisation
Proceedings of 3rd international workshop on Machine learning and music
On feature combination for music classification
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Non-negative tensor factorization applied to music genre classification
IEEE Transactions on Audio, Speech, and Language Processing
Genre classification and the invariance of MFCC features to key and tempo
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Parametric time-frequency analysis and its applications in music classification
EURASIP Journal on Advances in Signal Processing
Enhancing multi-label music genre classification through ensemble techniques
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
MULTIBOOST: a multi-purpose boosting package
The Journal of Machine Learning Research
Supervised dictionary learning for music genre classification
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Musical instrument identification based on new boosting algorithm with probabilistic decisions
CMMR'11 Proceedings of the 8th international conference on Speech, Sound and Music Processing: embracing research in India
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
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
Genre classification of symbolic music with SMBGT
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
Classification accuracy is not enough
Journal of Intelligent Information Systems
Music Recommendation Based on Multidimensional Description and Similarity Measures
Fundamenta Informaticae - To Andrzej Skowron on His 70th Birthday
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We present an algorithm that predicts musical genre and artist from an audio waveform. Our method uses the ensemble learner ADABOOST to select from a set of audio features that have been extracted from segmented audio and then aggregated. Our classifier proved to be the most effective method for genre classification at the recent MIREX 2005 international contests in music information extraction, and the second-best method for recognizing artists. This paper describes our method in detail, from feature extraction to song classification, and presents an evaluation of our method on three genre databases and two artist-recognition databases. Furthermore, we present evidence collected from a variety of popular features and classifiers that the technique of classifying features aggregated over segments of audio is better than classifying either entire songs or individual short-timescale features.