Prediction of Notes from Vocal Time Series Produced by Singing Voice

dc.contributor.author	Garczarek, Ursula	de
dc.contributor.author	Ligges, Uwe	de
dc.contributor.author	Weihs, Claus	de
dc.date.accessioned	2004-12-06T18:39:42Z
dc.date.available	2004-12-06T18:39:42Z
dc.date.issued	2003	de
dc.description.abstract	Aiming at optimal prediction of the correct note corresponding to a vocal time series we trained a classification algorithm on the basis of parts of interpretations of Tochter Zion (Händel) and tested the algorithm on the remaining parts. As classification algorithm we use a radial basis function support vector machine together with a “Hidden Markov” method as a dynamisation mechanism and some smoothing for categorical data. With this we were able to obtain a minimum of 5% average classification error and a maximum of 26% on data from an experiment with 16 singers.	en
dc.format.extent	309399 bytes
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/2003/4921
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-6842
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject	radial basis functions	en
dc.subject	support vector machines	en
dc.subject	classification	en
dc.subject	time series	en
dc.subject	prediction	en
dc.subject	singing voice	en
dc.subject.ddc	310	de
dc.title	Prediction of Notes from Vocal Time Series Produced by Singing Voice	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access

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