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Evolutionary Optimization of Music Performance Annotation

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dc.creator Grachten, Maarten
dc.creator Arcos, Josep Ll.
dc.creator Lopez de Mantaras, Ramon
dc.date 2008-03-13T10:12:50Z
dc.date 2008-03-13T10:12:50Z
dc.date 2005
dc.date.accessioned 2017-01-31T01:00:44Z
dc.date.available 2017-01-31T01:00:44Z
dc.identifier Computer Music Modeling and Retrieval, Second International Symposium, CMMR 2004, Esbjerg, Denmark, May 26-29, 2004. Revised Papers, Lecture Notes in Computer Science, Vol. 3310, p.p.: 347-358, Springer Berlin, 2005.
dc.identifier 978-3-540-24458-5
dc.identifier 0302-9743
dc.identifier http://hdl.handle.net/10261/3219
dc.identifier.uri http://dspace.mediu.edu.my:8181/xmlui/handle/10261/3219
dc.description The original publication is available at http://www.springerlink.com
dc.description In this paper we present an enhancement of edit distance based music performance annotation. The annotation captures musical expressivity not only in terms of timing deviations but also represents e.g. spontaneous note ornamentation. To reduce the number of errors in automatic performance annotation, some optimization is essential. We have taken an evolutionary approach to optimize the parameter values of cost functions of the edit distance. Automatic optimization is desirable since manual parameter tuning is unfeasible when more than a few performances are taken into account. The validity of the optimized parameter settings is shown by assessing their error-percentage on a test set
dc.description This research has been partially supported by the Spanish Ministry of Science and Technology under the project TIC 2003-07776-C2-02 “CBR-ProMusic: Content-based Music Processing using CBR” and EU-FEDER funds.
dc.description Peer reviewed
dc.format 226190 bytes
dc.format application/pdf
dc.language eng
dc.publisher Springer
dc.rights openAccess
dc.subject Artificial Intelligence
dc.subject Case-Based Reasoning
dc.subject CBR
dc.title Evolutionary Optimization of Music Performance Annotation
dc.type Artículo


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