Melizmų sintezė dirbtinių neuronų tinklais
Modern methods of speech synthesis are not suitable for restoration of song signals due to lack of vitality and intonation in the resulted sounds. The aim of presented work is to synthesize melismas met in Lithuanian folk songs, by applying Artificial Neural Networks. An analytical survey of rather...
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Lithuanian Academic Libraries Network (LABT)
2007
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Online Access: | http://vddb.library.lt/fedora/get/LT-eLABa-0001:E.02~2006~D_20070112_151232-71106/DS.005.1.01.ETD |
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ndltd-LABT_ETD-oai-elaba.lt-LT-eLABa-0001-E.02~2006~D_20070112_151232-711062014-01-17T03:46:04Z2007-01-12engElectronics and Electrical EngineeringLeonavičius, RomasMelizmų sintezė dirbtinių neuronų tinklaisMelisma Synthesis Using Artificial Neural NetworksLithuanian Academic Libraries Network (LABT)Modern methods of speech synthesis are not suitable for restoration of song signals due to lack of vitality and intonation in the resulted sounds. The aim of presented work is to synthesize melismas met in Lithuanian folk songs, by applying Artificial Neural Networks. An analytical survey of rather a widespread literature is presented. First classification and comprehensive discussion of melismas are given. The theory of dynamic systems which will make the basis for studying melismas is presented and finally the relationship for modeling a melisma with nonlinear and dynamic systems is outlined. Investigation of the most widely used Linear Prediction Coding method and possibilities of its improvement. The modification of original Linear Prediction method based on dynamic LPC frame positioning is proposed. On its basis, the new melisma synthesis technique is presented. Developed flexible generalized melisma model, based on two Artificial Neural Networks – a Multilayer Perceptron and Adaline – as well as on two network training algorithms – Levenberg- Marquardt and the Least Squares error minimization – is presented. Moreover, original mathematical models of Fortis, Gruppett, Mordent and Trill are created, fit for synthesizing melismas, and their minimal sizes are proposed. The last chapter concerns experimental investigation, using over 500 melisma records, and corroborates application of the new mathematical models to melisma synthesis of one performer.MelismaGrupetasMean opinion scoreForšlagasTrillMordentasDirbtinių neuronų tinklaiVNRTPKSongSynthesisMelizmaAdalinaVidutinės nuomonės rezultatasArtificial neural networkGruppettLinear prediction codingLPCDNTDaugiasluoksnis perceptronasDainaANTMOSLevenberg-MarquardtMultilayer perceptronMordentTiesinės prognozės koeficientaiFortisAdalineSintezėTrelėDoctoral thesisPaulikas, ŠarūnasSkudutis, JuliusMartavičius, RomanasApanavičius, RomualdasNavakauskas, DaliusLipeika, Antanas LeonasKajackas, AlgimantasRudžionis, AlgimantasVilnius Gediminas Technical UniversityVilnius Gediminas Technical Universityhttp://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20070112_151232-71106LT-eLABa-0001:E.02~2006~D_20070112_151232-71106VGTU-LABT20070112-151232-71106http://vddb.library.lt/fedora/get/LT-eLABa-0001:E.02~2006~D_20070112_151232-71106/DS.005.1.01.ETDUnrestrictedapplication/pdf |
collection |
NDLTD |
language |
English |
format |
Doctoral Thesis |
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NDLTD |
topic |
Electronics and Electrical Engineering Melisma Grupetas Mean opinion score Foršlagas Trill Mordentas Dirbtinių neuronų tinklai VNR TPK Song Synthesis Melizma Adalina Vidutinės nuomonės rezultatas Artificial neural network Gruppett Linear prediction coding LPC DNT Daugiasluoksnis perceptronas Daina ANT MOS Levenberg-Marquardt Multilayer perceptron Mordent Tiesinės prognozės koeficientai Fortis Adaline Sintezė Trelė |
spellingShingle |
Electronics and Electrical Engineering Melisma Grupetas Mean opinion score Foršlagas Trill Mordentas Dirbtinių neuronų tinklai VNR TPK Song Synthesis Melizma Adalina Vidutinės nuomonės rezultatas Artificial neural network Gruppett Linear prediction coding LPC DNT Daugiasluoksnis perceptronas Daina ANT MOS Levenberg-Marquardt Multilayer perceptron Mordent Tiesinės prognozės koeficientai Fortis Adaline Sintezė Trelė Leonavičius, Romas Melizmų sintezė dirbtinių neuronų tinklais |
description |
Modern methods of speech synthesis are not suitable for restoration of song signals due to lack of vitality and intonation in the resulted sounds. The aim of presented work is to synthesize melismas met in Lithuanian folk songs, by applying Artificial Neural Networks. An analytical survey of rather a widespread literature is presented. First classification and comprehensive discussion of melismas are given. The theory of dynamic systems which will make the basis for studying melismas is presented and finally the relationship for modeling a melisma with nonlinear and dynamic systems is outlined. Investigation of the most widely used Linear Prediction Coding method and possibilities of its improvement. The modification of original Linear Prediction method based on dynamic LPC frame positioning is proposed. On its basis, the new melisma synthesis technique is presented. Developed flexible generalized melisma model, based on two Artificial Neural Networks – a Multilayer Perceptron and Adaline – as well as on two network training algorithms – Levenberg- Marquardt and the Least Squares error minimization – is presented. Moreover, original mathematical models of Fortis, Gruppett, Mordent and Trill are created, fit for synthesizing melismas, and their minimal sizes are proposed. The last chapter concerns experimental investigation, using over 500 melisma records, and corroborates application of the new mathematical models to melisma synthesis of one performer. |
author2 |
Paulikas, Šarūnas |
author_facet |
Paulikas, Šarūnas Leonavičius, Romas |
author |
Leonavičius, Romas |
author_sort |
Leonavičius, Romas |
title |
Melizmų sintezė dirbtinių neuronų tinklais |
title_short |
Melizmų sintezė dirbtinių neuronų tinklais |
title_full |
Melizmų sintezė dirbtinių neuronų tinklais |
title_fullStr |
Melizmų sintezė dirbtinių neuronų tinklais |
title_full_unstemmed |
Melizmų sintezė dirbtinių neuronų tinklais |
title_sort |
melizmų sintezė dirbtinių neuronų tinklais |
publisher |
Lithuanian Academic Libraries Network (LABT) |
publishDate |
2007 |
url |
http://vddb.library.lt/fedora/get/LT-eLABa-0001:E.02~2006~D_20070112_151232-71106/DS.005.1.01.ETD |
work_keys_str_mv |
AT leonaviciusromas melizmusintezedirbtiniuneuronutinklais AT leonaviciusromas melismasynthesisusingartificialneuralnetworks |
_version_ |
1716625330360287232 |