6533b823fe1ef96bd127e0c3
RESEARCH PRODUCT
Multi-Dimensional motivic pattern extraction founded on adaptive redundancy filtering
Olivier Lartillotsubject
Theoretical computer scienceVisual Arts and Performing ArtsRelation (database)Space (commercial competition)050105 experimental psychology060404 music[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI][INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing[STAT.ML]Statistics [stat]/Machine Learning [stat.ML][INFO.INFO-FL]Computer Science [cs]/Formal Languages and Automata Theory [cs.FL]Redundancy (engineering)0501 psychology and cognitive sciencesControl (linguistics)MathematicsParametric statistics[INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL][SHS.MUSIQ]Humanities and Social Sciences/Musicology and performing artsbusiness.industry05 social sciences06 humanities and the artsAutomation[INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD]Multi dimensionalNASuffixbusiness0604 artsMusicdescription
Abstract We present a computational model for discovering repeated patterns in symbolic representations of monodic music. Patterns are discovered through an incremental adaptive identification along a multi-dimensional parametric space. The difficulties of pattern discovery mainly come from combinatorial redundancies, that our model is able to control efficiently. A specificity relation is defined between pattern descriptions, unifying suffix and inclusion relations and enabling a filtering of redundant descriptions. Combinatorial proliferation caused by successive repetitions of patterns is managed using cyclic patterns. The modelling of these redundancy control mechanisms enables an automation of musicology-relevant analyses of musical databases.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2005-12-01 |