Efficient repeating pattern finding in music databases
Proceedings of the seventh international conference on Information and knowledge management
The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Representation and Discovery of Vertical Patterns in Music
ICMAI '02 Proceedings of the Second International Conference on Music and Artificial Intelligence
A unified model of structural organization in language and music
Journal of Artificial Intelligence Research
Automatic identification of music performers with learning ensembles
Artificial Intelligence
Learning regular expressions from noisy sequences
SARA'05 Proceedings of the 6th international conference on Abstraction, Reformulation and Approximation
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In this paper we present a stepwise method for the analysis of musical sequences. The starting point is either a MIDI file or the score of a piece of music. The result is a set of likely themes and motifs. The method relies on a pitch intervals representation of music and an event discovery system that extracts significant and repeated patterns from sequences. We report and discuss the results of a preliminary experimentation, and outline future enhancements.