Prediction of notes from vocal time series produced by singing voice
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.
Year of publication: |
2003
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Authors: | Garczarek, Ursula ; Weihs, Claus ; Ligges, Uwe |
Publisher: |
Dortmund : Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen |
Saved in:
freely available
Series: | Technical Report ; 2003,01 |
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Type of publication: | Book / Working Paper |
Type of publication (narrower categories): | Working Paper |
Language: | English |
Other identifiers: | 81773368X [GVK] hdl:10419/49328 [Handle] RePEc:zbw:sfb475:200301 [RePEc] |
Source: |
Persistent link: https://www.econbiz.de/10010306243
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