Elements of information theory
Elements of information theory
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Statistical methods for speech recognition
Statistical methods for speech recognition
Automatic chord recognition from audio using a supervised HMM trained with audio-from-symbolic data
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
Music genre classification using MIDI and audio features
EURASIP Journal on Applied Signal Processing
Discovery of distinctive patterns in music
Intelligent Data Analysis - Machine Learning and Music
Harmonic and instrumental information fusion for musical genre classification
Proceedings of 3rd international workshop on Machine learning and music
Feature selection in a cartesian ensemble of feature subspace classifiers for music categorisation
Proceedings of 3rd international workshop on Machine learning and music
Probabilistic and logic-based modelling of harmony
CMMR'10 Proceedings of the 7th international conference on Exploring music contents
Automatic Chord Estimation from Audio: A Review of the State of the Art
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
Genre-Based Music Language Modeling with Latent Hierarchical Pitman-Yor Process Allocation
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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Music genre meta-data is of paramount importance for the organisation of music repositories. People use genre in a natural way when entering a music store or looking into music collections. Automatic genre classification has become a popular topic in music information retrieval research both, with digital audio and symbolic data. This work focuses on the symbolic approach, bringing to music cognition some technologies, like the stochastic language models, already successfully applied to text categorisation. The representation chosen here is to model chord progressions as n-grams and strings and then apply perplexity and naive Bayes classifiers, respectively, in order to assess how often those structures are found in the target genres. Some genres and sub-genres among popular, jazz, and academic music have been considered, trying to investigate how far can we reach using harmonic information with these models. The results at different levels of the genre hierarchy for the techniques employed are presented and discussed.