A maximum entropy approach to natural language processing
Computational Linguistics
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Manipulation of music for melody matching
MULTIMEDIA '98 Proceedings of the sixth ACM international conference on Multimedia
Melodic matching techniques for large music databases
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 1)
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A comparison of algorithms for maximum entropy parameter estimation
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
A graphical model for chord progressions embedded in a psychoacoustic space
ICML '05 Proceedings of the 22nd international conference on Machine learning
Term context models for information retrieval
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Music retrieval: a tutorial and review
Foundations and Trends in Information Retrieval
Probabilistic models for melodic prediction
Artificial Intelligence
Comparing probabilistic models for melodic sequences
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Probabilistic melodic harmonization
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
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Recent interest in the area of music information retrieval and related technologies is exploding. However, very few of the existing techniques take advantage of recent developments in statistical modeling. In this paper we discuss an application of Random Fields to the problem of creating accurate yet flexible statistical models of polyphonic music. With such models in hand, the challenges of developing effective searching, browsing and organization techniques for the growing bodies of music collections may be successfully met. We offer an evaluation of these models in terms of perplexity and prediction accuracy, and show that random fields not only outperform Markov chains, but are much more robust in terms of overfitting.