Arbeitspapier
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.
- Language
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Englisch
- Bibliographic citation
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Series: Technical Report ; No. 2003,01
- Event
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Geistige Schöpfung
- (who)
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Garczarek, Ursula
Weihs, Claus
Ligges, Uwe
- Event
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Veröffentlichung
- (who)
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Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
- (where)
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Dortmund
- (when)
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2003
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Garczarek, Ursula
- Weihs, Claus
- Ligges, Uwe
- Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Time of origin
- 2003
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Prediction of Notes from Vocal Time Series Produced by Singing Voice
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Pareto-Optimality and Desirability Indices
Desirability to characterize process capability
Uncertainty of the optimum influence factor levels in multicriteria optimization using the concept of desirability
Business cycle prediction using support vector methods
Stability of multivariate representation of business cycles over time
Optimal vs. classical linear dimension reduction
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