EconStor >
Technische Universität Dortmund >
Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen, Technische Universität Dortmund >
Technical Reports, SFB 475: Komplexitätsreduktion in multivariaten Datenstrukturen, TU Dortmund >

Please use this identifier to cite or link to this item:
Title:Prediction of notes from vocal time series produced by singing voice PDF Logo
Authors:Garczarek, Ursula
Weihs, Claus
Ligges, Uwe
Issue Date:2003
Series/Report no.:Technical Report // Universität Dortmund, SFB 475 Komplexitätsreduktion in Multivariaten Datenstrukturen 2003,01
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.
Document Type:Working Paper
Appears in Collections:Technical Reports, SFB 475: Komplexitätsreduktion in multivariaten Datenstrukturen, TU Dortmund

Files in This Item:
File Description SizeFormat
385361165.pdf302.15 kBAdobe PDF
No. of Downloads: Counter Stats
Download bibliographical data as: BibTeX
Share on:

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.