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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Bibliographic Details
Main Author: Leonavičius, Romas
Other Authors: Paulikas, Šarūnas
Format: Doctoral Thesis
Language:English
Published: Lithuanian Academic Libraries Network (LABT) 2007
Subjects:
VNR
TPK
LPC
DNT
ANT
MOS
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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spelling 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
sources 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
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