Authors: Garczarek, Ursula
Ligges, Uwe
Weihs, Claus
Title: Prediction of Notes from Vocal Time Series Produced by Singing Voice
Language (ISO): en
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.
Subject Headings: radial basis functions
support vector machines
classification
time series
prediction
singing voice
URI: http://hdl.handle.net/2003/4921
http://dx.doi.org/10.17877/DE290R-6842
Issue Date: 2003
Provenance: Universitätsbibliothek Dortmund
Appears in Collections:Sonderforschungsbereich (SFB) 475

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