masters dissertaion using template with placeholders
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docs/chapter-style.sty
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docs/chapter-style.sty
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\usepackage{color}
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\definecolor{szary}{gray}{0.5}
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\SetSectionFormatting[breakbefore,wholewidth]{chapter}
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{0\p@}
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{\FormatRigidChapterHeading{4.4\baselineskip}{12\p@}%
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{\color{szary}\hrule\vspace{5mm}\hfill\large\@chapapp\space}{\hrule\vspace{10mm}\color{black}\LARGE}\vspace{2mm}\hrulefill\\\vspace{-4mm}\hrulefill}
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{1.6\baselineskip}
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\relax
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\relax
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\@writefile{toc}{\contentsline {section}{\numberline {1}Wst\IeC {\k e}p}{2}\protected@file@percent }
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\@writefile{toc}{\contentsline {chapter}{Streszczenie}{7}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {1.1}Muzyka}{2}\protected@file@percent }
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\@writefile{lof}{\addvspace {10\p@ }}
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\@writefile{toc}{\contentsline {section}{\numberline {2}MIDI, Muzyka jako Informacje}{2}\protected@file@percent }
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\@writefile{lot}{\addvspace {10\p@ }}
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.1}MIDI}{2}\protected@file@percent }
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\@writefile{toc}{\contentsline {chapter}{Abstract}{9}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.2}Pianoroll}{2}\protected@file@percent }
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\@writefile{lof}{\addvspace {10\p@ }}
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.3}Muzyka jako tr\IeC {\'o}jwymiarowa tablica}{2}\protected@file@percent }
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\@writefile{lot}{\addvspace {10\p@ }}
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\@writefile{toc}{\contentsline {section}{\numberline {3}Generatwne sieci neuronowe - GANy, VAE, LSTMy}{2}\protected@file@percent }
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\@writefile{toc}{\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 1\relax .\leavevmode@ifvmode \kern .5em Wst\IeC {\k e}p}{11}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.1}Autoencodery, VAE}{2}\protected@file@percent }
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\@writefile{lof}{\addvspace {10\p@ }}
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.2}LSTM}{2}\protected@file@percent }
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\@writefile{lot}{\addvspace {10\p@ }}
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\@writefile{toc}{\contentsline {section}{\numberline {4}Modele generatywne stosowane w generowaniu muzyki}{3}\protected@file@percent }
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\@writefile{toc}{\contentsline {section}{\numberline {1.1\relax .\leavevmode@ifvmode \kern .5em }Zastosowania uczenia maszynowego w muzyce}{11}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {4.1}Project Magenta}{3}\protected@file@percent }
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\@writefile{toc}{\contentsline {section}{\numberline {1.2\relax .\leavevmode@ifvmode \kern .5em }Modele generatywne}{11}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {4.2}MuseGAN}{3}\protected@file@percent }
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\@writefile{toc}{\contentsline {section}{\numberline {1.3\relax .\leavevmode@ifvmode \kern .5em }Muzyka symboliczna, a muzyka}{11}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {4.3}VAE-MIDI}{3}\protected@file@percent }
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\@writefile{toc}{\contentsline {section}{\numberline {1.4\relax .\leavevmode@ifvmode \kern .5em }Cele tej pracy.}{12}\protected@file@percent }
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\@writefile{toc}{\contentsline {section}{\numberline {5}Budowanie generatora muzyki}{3}\protected@file@percent }
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\@writefile{toc}{\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 2\relax .\leavevmode@ifvmode \kern .5em Reprezentacja muzyki}{13}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {5.1}Wyodr\IeC {\k e}bnienie danych z plik\IeC {\'o}w MIDI}{3}\protected@file@percent }
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\@writefile{lof}{\addvspace {10\p@ }}
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\@writefile{toc}{\contentsline {subsection}{\numberline {5.2}Przygotowanie Modelu GAN}{3}\protected@file@percent }
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\@writefile{lot}{\addvspace {10\p@ }}
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\@writefile{toc}{\contentsline {subsection}{\numberline {5.3}Proces uczenia, pr\IeC {\'o}bki co kilka epoch\IeC {\'o}w, costloss wykres}{3}\protected@file@percent }
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\@writefile{toc}{\contentsline {section}{\numberline {2.1\relax .\leavevmode@ifvmode \kern .5em }Podstawowe koncepcje}{13}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {5.4}Pr\IeC {\'o}bki ko\IeC {\'n}cowe, jak\IeC {\k a} muzyk\IeC {\k e} da si\IeC {\k e} z tego wygenerowa\IeC {\'c}}{3}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.1.1\relax .\leavevmode@ifvmode \kern .5em }Nuta}{13}\protected@file@percent }
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\@writefile{toc}{\contentsline {section}{\numberline {6}Podsumowanie}{3}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.1.2\relax .\leavevmode@ifvmode \kern .5em }Skala}{13}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.1.3\relax .\leavevmode@ifvmode \kern .5em }Akord}{13}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.1.4\relax .\leavevmode@ifvmode \kern .5em }Utw\IeC {\'o}r}{14}\protected@file@percent }
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\@writefile{toc}{\contentsline {section}{\numberline {2.2\relax .\leavevmode@ifvmode \kern .5em }Reprezentacja muzyki}{14}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.2.1\relax .\leavevmode@ifvmode \kern .5em }Zapis klasyczny - pi\IeC {\k e}ciolinia}{14}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.2.2\relax .\leavevmode@ifvmode \kern .5em }Tabulatura}{14}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.2.3\relax .\leavevmode@ifvmode \kern .5em }Pianoroll}{14}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {2.2.4\relax .\leavevmode@ifvmode \kern .5em }Tekstowa}{15}\protected@file@percent }
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\@writefile{toc}{\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 3\relax .\leavevmode@ifvmode \kern .5em Sieci neuronowe}{17}\protected@file@percent }
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\@writefile{lof}{\addvspace {10\p@ }}
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\@writefile{lot}{\addvspace {10\p@ }}
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.1\relax .\leavevmode@ifvmode \kern .5em }Wst\IeC {\k e}p do sieci neuronowych, definicje wzory itp.}{17}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.2\relax .\leavevmode@ifvmode \kern .5em }Autoencodery}{17}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.3\relax .\leavevmode@ifvmode \kern .5em }LSTM}{17}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.4\relax .\leavevmode@ifvmode \kern .5em }GAN}{18}\protected@file@percent }
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\@writefile{toc}{\contentsline {chapter}{Budowanie generatora muzyki}{19}\protected@file@percent }
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\@writefile{lof}{\addvspace {10\p@ }}
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\@writefile{lot}{\addvspace {10\p@ }}
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.5\relax .\leavevmode@ifvmode \kern .5em }Przygotowanie danych}{19}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.6\relax .\leavevmode@ifvmode \kern .5em }Architektura sieci neuronowej}{19}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.7\relax .\leavevmode@ifvmode \kern .5em }Proces treningowy}{19}\protected@file@percent }
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\@writefile{toc}{\contentsline {subsection}{\numberline {3.0.8\relax .\leavevmode@ifvmode \kern .5em }Przyk\IeC {\l }ady wygenerowanej muzyki}{20}\protected@file@percent }
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\@writefile{toc}{\contentsline {chapter}{Podsumowanie}{21}\protected@file@percent }
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\@writefile{lof}{\addvspace {10\p@ }}
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\@writefile{lot}{\addvspace {10\p@ }}
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\bibcite{}{1}
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\bibcite{}{2}
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\bibcite{}{3}
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\@writefile{toc}{\contentsline {chapter}{Bibliografia}{23}\protected@file@percent }
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\@writefile{lof}{\addvspace {10\p@ }}
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\@writefile{lot}{\addvspace {10\p@ }}
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98
docs/document.fdb_latexmk
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docs/document.fdb_latexmk
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# Fdb version 3
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["makeindex document.idx"] 1560413097 "document.idx" "document.ind" "document" 1560413099
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"document.idx" 1560413012 0 d41d8cd98f00b204e9800998ecf8427e ""
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(generated)
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"document.ind"
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"document.ilg"
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["pdflatex"] 1560413098 "document.tex" "document.pdf" "document" 1560413099
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"c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plit.enc" 1550343089 1946 62ba825cda1ff16dbaa60d53ac60525a ""
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"c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plmi.enc" 1550343089 2005 f094775651a1386335c158fb632529f1 ""
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"c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plrm.enc" 1550343089 1944 1845592c00bbe510359fd6887a83ff72 ""
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"c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plsc.enc" 1550343089 1970 17f08459267325f59652d212eb7ed719 ""
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"c:/software/latex/texmf-dist/fonts/map/fontname/texfonts.map" 1550341350 3332 103109f5612ad95229751940c61aada0 ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/amsfonts/symbols/msam10.tfm" 1550339660 916 f87d7c45f9c908e672703b83b72241a3 ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/amsfonts/symbols/msam7.tfm" 1550339660 928 2dc8d444221b7a635bb58038579b861a ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/amsfonts/symbols/msbm10.tfm" 1550339660 908 2921f8a10601f252058503cc6570e581 ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/amsfonts/symbols/msbm7.tfm" 1550339660 940 228d6584342e91276bf566bcf9716b83 ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/cm/cmr10.tfm" 1550340360 1296 45809c5a464d5f32c8f98ba97c1bb47f ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/cm/cmr12.tfm" 1550340360 1288 655e228510b4c2a1abe905c368440826 ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plbx10.tfm" 1550343089 2120 c2bca6a74408b1687f9d32e4fd3978fc ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plbx12.tfm" 1550343089 2112 e31d5b368c33d42906fcc26c9ca0a59c ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plcsc10.tfm" 1550343089 2140 d2ca82625bedca0b1948a5da5764d911 ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plex10.tfm" 1550343089 992 93098cecc00cfdb7e881cb8983ca7545 ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plmi10.tfm" 1550343089 1528 c9811a5c54036a0d60617f9ae77b990e ""
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||||||
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plmi12.tfm" 1550343089 1524 234f65aa0171d619f32097d0f9186df0 ""
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||||||
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plmi6.tfm" 1550343089 1512 01fe68a80e3d13383410f67b67ce344d ""
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||||||
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plmi8.tfm" 1550343089 1520 1a3ee2ce8d697eaf56e43f8d2c40cb71 ""
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||||||
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plr10.tfm" 1550343089 2080 5c3eb4fec3b89def05dc3a4c6b93fb7c ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plr12.tfm" 1550343089 2076 e1758d4caaf275a731cf0532450f26f0 ""
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||||||
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plr17.tfm" 1550343089 2080 b61c70f1a22b45d92358c39b67ea9906 ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plr6.tfm" 1550343089 2088 aa80c569a200447488b571f8bf7b132b ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plr8.tfm" 1550343089 2080 290449bfe12d41e2714fa828fd909bf8 ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plsy10.tfm" 1550343089 1308 adee1faadc465f51667a741ced3a8365 ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plsy6.tfm" 1550343089 1300 730d55d47130c8c9a62c9e7eab4ae96a ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plsy8.tfm" 1550343089 1304 3b6e6a90176144a582c7bcae45156f9b ""
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"c:/software/latex/texmf-dist/fonts/tfm/public/pl/plti10.tfm" 1550343089 2240 33876944658f33d5fc3eacb794ebb2f3 ""
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||||||
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"c:/software/latex/texmf-dist/fonts/type1/public/pl/plbx10.pfb" 1550343089 27050 0da5f310861fb948b3c5e6a53aad685b ""
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"c:/software/latex/texmf-dist/fonts/type1/public/pl/plbx12.pfb" 1550343089 26733 3fa55bab1df289b642fa8cd9e61fb25f ""
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"c:/software/latex/texmf-dist/fonts/type1/public/pl/plcsc10.pfb" 1550343089 26854 ae64d555ccf6d86e33627b80279e1347 ""
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"c:/software/latex/texmf-dist/fonts/type1/public/pl/plmi12.pfb" 1550343089 29704 59e409fb948cf911862e0a97f2ae41a4 ""
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"c:/software/latex/texmf-dist/fonts/type1/public/pl/plmi8.pfb" 1550343089 29648 b38cd15b84b56d7bea1291ffd32de967 ""
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"c:/software/latex/texmf-dist/tex/generic/oberdiek/ifluatex.sty" 1550341739 7324 2310d1247db0114eb4726807c8837a0e ""
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"c:/software/latex/texmf-dist/tex/generic/oberdiek/ifpdf.sty" 1550342831 1300 96620a7d94bc0ceb261d968770ce8315 ""
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"c:/software/latex/texmf-dist/tex/generic/oberdiek/infwarerr.sty" 1550342831 8253 473e0e41f9adadb1977e8631b8f72ea6 ""
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"c:/software/latex/texmf-dist/tex/latex/amsmath/amsmath.sty" 1550339666 85093 77cfa18ddbae4d9b74ade33d843686d3 ""
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"c:/software/latex/texmf-dist/tex/latex/amsmath/amsopn.sty" 1550339666 4116 32e6abd27229755a83a8b7f18e583890 ""
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"c:/software/latex/texmf-dist/tex/latex/graphics-cfg/color.cfg" 1550341562 1213 620bba36b25224fa9b7e1ccb4ecb76fd ""
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"c:/software/latex/texmf-dist/tex/latex/latexconfig/epstopdf-sys.cfg" 1550342073 678 4792914a8f45be57bb98413425e4c7af ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/mwcls/mwbk.cls" 1550342667 38805 274df96ed3aa756c4c77df610bde271a ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/mwcls/mwbk12.clo" 1550342667 5794 8b98de20f2007e1656b90be311a3ea14 ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/oberdiek/epstopdf-base.sty" 1550342831 12095 5337833c991d80788a43d3ce26bd1c46 ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/oberdiek/grfext.sty" 1550342831 7075 2fe3d848bba95f139de11ded085e74aa ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/oberdiek/kvoptions.sty" 1550342832 22417 1d9df1eb66848aa31b18a593099cf45c ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/polski/omlplm.fd" 1550343136 1553 7e55eec7e7ffbbc85187034f06b5977a ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/polski/omsplsy.fd" 1550343136 1472 cdbddbc0209f1dca4b38ccca913f65d7 ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/polski/omxplex.fd" 1550343136 1283 a1205ac8611390c6de04f8d76756c3c4 ""
|
||||||
|
"c:/software/latex/texmf-dist/tex/latex/polski/ot4cmr.fd" 1550343136 2599 8c16345f9109faed2ca2ff52e16d8a6f ""
|
||||||
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"c:/software/latex/texmf-dist/tex/latex/polski/polski.sty" 1550343136 13310 401406f54546887504ce1857a307089b ""
|
||||||
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"c:/software/latex/texmf-dist/web2c/texmf.cnf" 1550341956 35176 ce7fee66be1fb01dfc6b702a7dbfa0ea ""
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||||||
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"c:/software/latex/texmf-var/fonts/map/pdftex/updmap/pdftex.map" 1550344777 3265577 7fb0ae9195d4a898deb882ebc26f3a7b ""
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||||||
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||||||
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"c:/software/latex/texmf.cnf" 1550344763 715 839ef4b14d931ce86420174d3a223d3e ""
|
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"chapter-style.sty" 1184432756 376 87871882de090607c810d89fd5ae18d9 ""
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"document.tex" 1560413089 20828 a07a211563afcd6d5b92063e4323203d ""
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|
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|
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|
||||||
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||||||
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|
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|
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|
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|
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|
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|
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|
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|
||||||
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|
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|
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|
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|
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|
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|
|||||||
This is pdfTeX, Version 3.14159265-2.6-1.40.19 (TeX Live 2018/W32TeX) (preloaded format=pdflatex 2019.2.21) 28 MAY 2019 12:32
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This is pdfTeX, Version 3.14159265-2.6-1.40.19 (TeX Live 2018/W32TeX) (preloaded format=pdflatex 2019.2.16) 13 JUN 2019 10:04
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||||||
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|
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|
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|
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|
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|
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|
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**document.tex
|
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|
||||||
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|
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|
||||||
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|
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|
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|
||||||
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|
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|
||||||
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|
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|
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|
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|
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|
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\documentclass[utf8]{article}
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\documentclass[12pt,a4paper,reqno,twoside]{mwbk}
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\usepackage{polski}
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\title{%
|
\textheight 21.1 cm
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Generowanie muzyki \\
|
|
||||||
przy pomocy głębokiego uczenia \\
|
|
||||||
\large Music generation with deep learning}
|
|
||||||
|
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||||||
|
\voffset = 1.2 cm
|
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|
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||||||
\author{%
|
\textwidth 14 cm
|
||||||
Cezary Pukownik \\
|
|
||||||
\newline
|
|
||||||
\small Opiekun pracy:\\
|
|
||||||
dr hab. Tomasz Górecki}
|
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\date{2019-05-28}
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% Strona tytułowa
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\begin{center}
|
||||||
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\textsc{UNIWERSYTET IM. ADAMA MICKIEWICZA W POZNANIU}
|
||||||
|
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||||||
|
\vglue 0.1 cm
|
||||||
|
|
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\textsc{WYDZIAŁ MATEMATYKI I INFORMATYKI}
|
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\vglue 2.1 cm
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|
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{\LARGE \bf Cezary Adam Pukownik}
|
||||||
|
|
||||||
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\vglue 1cm
|
||||||
|
|
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{\large Kierunek: Analiza i przetwarzanie danych}
|
||||||
|
|
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\medskip
|
||||||
|
|
||||||
|
{\large Specjalność: Uczenie maszynowe}
|
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|
|
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\medskip
|
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|
|
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{\large Numer albumu: 444337}
|
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\vspace{1.5cm}
|
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|
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{\Huge \bf Generowanie muzyki \\[4pt] przy pomocy głębokiego uczenia\\}
|
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|
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||||||
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\vspace{0.8cm}
|
||||||
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{\large \bf Music generation with deep learning\\}
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|
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|
\end{center}
|
||||||
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|
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|
\vspace{3cm}
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\hspace{7.5cm}{Praca licencjacka}\\[-12pt]
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\hspace{7.5cm}{napisana pod kierunkiem}\\[-12pt]
|
||||||
|
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|
\hspace{7.5cm}{dr hab. Tomasza Góreckiego}
|
||||||
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\vspace{2.2cm}
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\begin{center}
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|
\textsc{POZNAŃ 2020}
|
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|
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|
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|
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|
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|
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|
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|
Poznań, dnia .....................
|
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|
\end{flushright}
|
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|
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|
\vglue 2.4 cm
|
||||||
|
|
||||||
|
\begin{center}
|
||||||
|
\large \bf OŚWIADCZENIE
|
||||||
|
\end{center}
|
||||||
|
|
||||||
|
\vglue 1.2 cm
|
||||||
|
|
||||||
|
Ja, niżej podpisany Cezary Pukownik, student Wydziału Matematyki i Informatyki Uniwersytetu im. Adama Mickiewicza w Poznaniu oświadczam, że przedkładaną pracę dyplomową pt: "Generowanie muzyki przy pomocy głębokiego uczenia", napisałem samodzielnie. Oznacza to, że przy pisaniu pracy, poza niezbędnymi konsultacjami, nie korzystałem z pomocy innych osób, a w szczególności nie zlecałem opracowania rozprawy lub jej części innym osobom, ani nie odpisywałem tej rozprawy lub jej części od innych osób.
|
||||||
|
|
||||||
|
Oświadczam również, że egzemplarz pracy dyplomowej w wersji drukowanej jest całkowicie zgodny z egzemplarzem pracy dyplomowej w wersji elektronicznej.
|
||||||
|
|
||||||
|
Jednocześnie przyjmuję do wiadomości, że przypisanie sobie, w pracy dyplomowej, autorstwa istotnego fragmentu lub innych elementów cudzego utworu lub ustalenia naukowego stanowi podstawę stwierdzenia nieważności postępowania w sprawie nadania tytułu zawodowego.
|
||||||
|
|
||||||
|
\bigskip
|
||||||
|
|
||||||
|
\noindent $[TAK]^{\star}$ - wyrażam zgodę na udostępnianie mojej pracy w czytelni Archiwum UAM
|
||||||
|
|
||||||
|
\medskip
|
||||||
|
|
||||||
|
\noindent $[TAK]^{\star}$ - wyrażam zgodę na udostępnianie mojej pracy w zakresie koniecznym do ochrony mojego prawa do autorstwa lub praw osób trzecich
|
||||||
|
|
||||||
|
\vglue 1.2 cm
|
||||||
|
|
||||||
|
\noindent{\small $^{\star}$Należy wpisać TAK w przypadku wyrażenia zgody na udostępnianie pracy w czytelni Archiwum UAM, NIE w przypadku braku zgody. Niewypełnienie pola oznacza brak zgody na udostępnianie pracy.}
|
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% Koniec oświadczenia
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\clearpage \thispagestyle{empty} \cleardoublepage
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\newpage
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\pagenumbering{arabic}
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\tableofcontents
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\tableofcontents
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\chapter*{Streszczenie}
|
||||||
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Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
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\chapter*{Abstract}
|
||||||
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|
||||||
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Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
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|
\chapter{Wstęp}
|
||||||
|
|
||||||
|
Uczenie maszynowe w ostatnich latach mocno zyskało na popularności. Zastosowania i możliwości różnych algorytmów Mashine Learning czasami przekraczają nasze wyobrażenie o tym, co komputer może zrobić. Niektóre aplikacje potrafią wręcz zaskoczyć użytkowników tym, co potrafią zrobić. Wśród takich aplikacji znajdują się takie, które potrafią przewidywać następne wartości akcji giełdowych, rozpoznawać na filmie obiekty w czasie rzeczywistym czy nawet prowadzić samochód. Algorytmy wyuczone proponują nam spersonalizowane reklamy, czy produkty na podstawie naszych upodobań. Najczęstsze zastosowania dotyczą przetwarzania obrazów lub tekstu, natomiast zastosowania w przetwarzaniu muzyki są niszowe i rzadko spotykane.
|
||||||
|
|
||||||
|
\section{Zastosowania uczenia maszynowego w muzyce}
|
||||||
|
Wśród najbardziej rozwiniętych zastosowań uczenia maszynowego w muzyce, można wymienić algorytmy polecania utworów w portalach streamingowych takich jak Spotify czy Tidal. Algorytmy potrafią znajdywać podobne do siebie utwory i polecać je nam na podstawie naszych preferencji.
|
||||||
|
|
||||||
|
\section{Modele generatywne}
|
||||||
|
Jednym z najnowszych modeli sieci neuronowych są sieci generatywne. Wychodzą one poza standardowe zastosowania klasyfikacji i regresji. Modele generatywne, uczą się ze zbioru uczącego najważniejszych ale i ogólnych cech i potrafią reprodukować podobne wyniki.
|
||||||
|
|
||||||
|
\section{Muzyka symboliczna, a muzyka}
|
||||||
|
Należy rozróżnić dwa pojęcia, które są od siebie różne w podstawowych założeniach. Muzyka symboliczna, i muzyka odegrana. Muzyka symboliczna, jest to utwór zapisany, skomponowany ale na papierze. Przedstawia to muzykę jako koncepcję, taki przepis na utwór. Taka muzyka zapisywana jest klasycznie na pięciolinii, czy komputerowo przy pomocy protokołu MIDI. Druga muzyka, jest to muzyka już odebrana, która nie przechowuje informacji o tym jak zagrać, czy odtworzyć utwór muzyczny, ale brzmienie tego utworu jako fala dźwiękowa. W tej pracy będę opisywał przede wszystkim generowanie muzyki symbolicznej.
|
||||||
|
|
||||||
|
\section{Cele tej pracy.}
|
||||||
|
Celem tej pracy, jest zastosowanie technik głębokiego uczenia, do generowania muzyki. Jest to bardzo ogólny cel, ponieważ muzyką może być prosta melodia oparta na kilku dźwiękach grana przez jeden instrument ale również aranżacja orkiestralna na wiele instrumentów, które razem współgrają i wybrzmiewają jako jeden pełny utwór.
|
||||||
|
|
||||||
|
|
||||||
\newpage
|
\newpage
|
||||||
|
|
||||||
|
\chapter{Reprezentacja muzyki}
|
||||||
|
Muzyka jesy
|
||||||
|
|
||||||
\section{Wstęp}
|
\section{Podstawowe koncepcje}
|
||||||
To jest wstep do pracy magisterskiej
|
\subsection{Nuta}
|
||||||
|
Każdy utwór muzyczny składa się nut. Nuta jest podstawowym obiektem w muzycznym słowniku. Każda nuta ma dwa parametry, wartości oraz wysokości. Wartość nuty określa jak długo będzie ona trwać w czasie, relatywnie do pozostałych nut. Wysokość noty oznacza z jaką częstotliwością fala dzwiękowa tej nuty ma wybrzmieć. Częstotliwości te są nazwane literami alfabetu ABCDEFG lub w zapisie polskim AHCDEFG.
|
||||||
|
|
||||||
\subsection{Muzyka}
|
\subsection{Skala}
|
||||||
Teraz opowiem troche o muzyce, i dlaczego trudno jest ja generowac, co o tym sądze, oraz czy sztuczna inteligencja zastapi muzyków w przyszłości.
|
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut fermentum lorem libero. Duis a magna arcu. Nam sit amet porta odio. Cras sit amet euismod elit. Etiam a turpis eget magna pharetra malesuada. Vivamus accumsan leo eget turpis efficitur, non interdum tortor pretium. Maecenas at massa nec elit imperdiet sagittis. Maecenas pellentesque libero et risus aliquam consectetur.
|
||||||
|
|
||||||
\section{MIDI, Muzyka jako Informacje}
|
|
||||||
Tutaj opiszę w jaki sposób muzyka jest zapisywana jako informacje komputerowe, protokuł midi, przedstawienie muzyki jako pianorolle.
|
|
||||||
|
|
||||||
\subsection{MIDI}
|
\subsection{Akord}
|
||||||
Tutaj opiszę protokuł MIDI
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
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\subsection{Utwór}
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Aenean malesuada interdum hendrerit. Integer quis nisl et neque iaculis dapibus at in metus. Cras pretium bibendum magna at aliquet. Integer aliquet cursus augue, efficitur sollicitudin felis fringilla efficitur. Vivamus euismod bibendum justo, vitae suscipit nunc mattis a. Sed egestas porttitor velit, sit amet volutpat tortor suscipit vitae. Nulla nec dignissim mauris. Curabitur maximus viverra mollis. Suspendisse molestie turpis sit amet turpis interdum viverra ac eu lorem. Suspendisse iaculis ultricies ante, a condimentum odio congue nec. Integer varius lobortis diam, eget scelerisque nisl mattis at.
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||||||
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\section{Reprezentacja muzyki}
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||||||
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\subsection{Zapis klasyczny - pięciolinia}
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||||||
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Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
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\subsection{Tabulatura}
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Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
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\subsection{Pianoroll}
|
\subsection{Pianoroll}
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Tutaj opisze co todsdsddsdss są pianorolle, jak je czytać i czemu służą.
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
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\subsection{Muzyka jako trójwymiarowa tablica}
|
\subsection{Tekstowa}
|
||||||
Tutaj opisze dlaczego muzykę moża opisać jako trójwymiarowa tablicę.
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
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|
||||||
\section{Generatwne sieci neuronowe - GANy, VAE, LSTMy}
|
\chapter{Sieci neuronowe}
|
||||||
Tutaj będzie opisane, dlaczego sieci neuronowe, radzą sobie lepiej w produkowaniu muzyki niż inne modele. Oraz jakie modele są odpowidnie do pewnych zastosowań, JAZZ - LSTM, bardziej ustrukturyzowana - VAE itp.
|
Tutaj będzie opisane, dlaczego sieci neuronowe, radzą sobie lepiej w produkowaniu muzyki niż inne modele. Oraz jakie modele są odpowidnie do pewnych zastosowań, JAZZ - LSTM, bardziej ustrukturyzowana - VAE itp.
|
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|
||||||
\subsection{Autoencodery, VAE}
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\subsection{Wstęp do sieci neuronowych, definicje wzory itp.}
|
||||||
Teraz opowiem troche o muzyce, i dlaczego trudno jest ja generowac
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
|
\subsection{Autoencodery}
|
||||||
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
\subsection{LSTM}
|
\subsection{LSTM}
|
||||||
Teraz opowiem troche o muzyce, i dlaczego trudno jest ja generowac
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
\section{Modele generatywne stosowane w generowaniu muzyki}
|
\subsection{GAN}
|
||||||
Przykłady gotowych podeść do generowania muzyki, oraz jakie modele zostały zastosowane. dlaczego takie itp.
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
\subsection{Project Magenta}
|
|
||||||
Teraz opowiem troche o muzyce, i dlaczego trudno jest ja generowac
|
|
||||||
|
|
||||||
\subsection{MuseGAN}
|
|
||||||
Teraz opowiem troche o muzyce, i dlaczego trudno jest ja generowac
|
|
||||||
|
|
||||||
\subsection{VAE-MIDI}
|
|
||||||
Teraz opowiem troche o muzyce, i dlaczego trudno jest ja generowac
|
|
||||||
|
|
||||||
|
|
||||||
\section{Budowanie generatora muzyki}
|
\chapter*{Budowanie generatora muzyki}
|
||||||
W tym rozdzialę opiszę w jaki sposób zbudowałem swój własny geneator muzyki, jak przechodził procesz uczenia, jakie próbki udało mi się wygenrować. Opis kodu który napisałem.
|
W tym rozdzialę opiszę w jaki sposób zbudowałem swój własny geneator muzyki, jak przechodził procesz uczenia, jakie próbki udało mi się wygenrować. Opis kodu który napisałem.
|
||||||
|
|
||||||
\subsection{Wyodrębnienie danych z plików MIDI}
|
\subsection{Przygotowanie danych}
|
||||||
\subsection{Przygotowanie Modelu GAN}
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
\subsection{Proces uczenia, próbki co kilka epochów, costloss wykres}
|
\subsection{Architektura sieci neuronowej}
|
||||||
\subsection{Próbki końcowe, jaką muzykę da się z tego wygenerować}
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
\subsection{Proces treningowy}
|
||||||
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
\subsection{Przykłady wygenerowanej muzyki}
|
||||||
|
Proin ac dui orci. Cras nec elit eleifend lacus eleifend gravida. Ut placerat lacinia dolor non viverra. Curabitur rhoncus sit amet nibh sed malesuada. Integer iaculis eros venenatis, tempor enim non, sollicitudin sapien. Vestibulum eu scelerisque erat. Pellentesque habitant morbi tristique senectus et netus et malesuada fames ac turpis egestas. Nulla at felis massa. Ut est arcu, rhoncus ac tincidunt vel, consequat eu sem. Aliquam neque orci, lacinia molestie enim fermentum, ullamcorper congue mauris. Phasellus pellentesque, ante nec ultricies porta, erat erat placerat ante, vitae vehicula ipsum enim id ante. Donec malesuada tortor id ornare mattis. Nulla nec augue at augue dictum aliquet.
|
||||||
|
|
||||||
\section{Podsumowanie}
|
\chapter*{Podsumowanie}
|
||||||
Ostateczne wnioski, czy muzyka generowana komputerowa da się lubić? Czy to pozytywnie wpłynie na przemysł muzyczny? Tak i nie. Może złużyć jako inspiracja dla muzyków, proces wspierający. Z drugiej strony może obnizy koszty produkowania muzyki pop, która i tak jest już bardzo powtarzalna. Czy sieci neuronowe nauczą się produkować Hity?
|
Ostateczne wnioski, czy muzyka generowana komputerowa da się lubić? Czy to pozytywnie wpłynie na przemysł muzyczny? Tak i nie. Może złużyć jako inspiracja dla muzyków, proces wspierający. Z drugiej strony może obnizy koszty produkowania muzyki pop, która i tak jest już bardzo powtarzalna. Czy sieci neuronowe nauczą się produkować Hity?
|
||||||
|
|
||||||
|
|
||||||
|
\begin{thebibliography}{99}
|
||||||
|
|
||||||
|
\bibitem{} Briot, J.P., Hadjeres, G., Pachet, F.D. (2019): {\em Deep Learning Techniques for Music Generation - A Survey. arXiv:1709.01620v3}
|
||||||
|
\bibitem{} Goodfellow, I., Bengio, Y., Courville, A. (2016): {\em Deep Learning. MIT Press.}
|
||||||
|
\bibitem{} Zocca, V., Spacagna, G., Slater, D., Roelants, P. (2018): {\em Deep Learning. Uczenie głębokie z językiem Python. Helion.}
|
||||||
|
\end{thebibliography}
|
||||||
|
|
||||||
|
|
||||||
|
\printindex
|
||||||
|
|
||||||
\end{document}
|
\end{document}
|
||||||
@ -1,19 +1,30 @@
|
|||||||
\contentsline {section}{\numberline {1}Wst\IeC {\k e}p}{2}%
|
\contentsline {chapter}{Streszczenie}{7}%
|
||||||
\contentsline {subsection}{\numberline {1.1}Muzyka}{2}%
|
\contentsline {chapter}{Abstract}{9}%
|
||||||
\contentsline {section}{\numberline {2}MIDI, Muzyka jako Informacje}{2}%
|
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 1\relax .\leavevmode@ifvmode \kern .5em Wst\IeC {\k e}p}{11}%
|
||||||
\contentsline {subsection}{\numberline {2.1}MIDI}{2}%
|
\contentsline {section}{\numberline {1.1\relax .\leavevmode@ifvmode \kern .5em }Zastosowania uczenia maszynowego w muzyce}{11}%
|
||||||
\contentsline {subsection}{\numberline {2.2}Pianoroll}{2}%
|
\contentsline {section}{\numberline {1.2\relax .\leavevmode@ifvmode \kern .5em }Modele generatywne}{11}%
|
||||||
\contentsline {subsection}{\numberline {2.3}Muzyka jako tr\IeC {\'o}jwymiarowa tablica}{2}%
|
\contentsline {section}{\numberline {1.3\relax .\leavevmode@ifvmode \kern .5em }Muzyka symboliczna, a muzyka}{11}%
|
||||||
\contentsline {section}{\numberline {3}Generatwne sieci neuronowe - GANy, VAE, LSTMy}{2}%
|
\contentsline {section}{\numberline {1.4\relax .\leavevmode@ifvmode \kern .5em }Cele tej pracy.}{12}%
|
||||||
\contentsline {subsection}{\numberline {3.1}Autoencodery, VAE}{2}%
|
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 2\relax .\leavevmode@ifvmode \kern .5em Reprezentacja muzyki}{13}%
|
||||||
\contentsline {subsection}{\numberline {3.2}LSTM}{2}%
|
\contentsline {section}{\numberline {2.1\relax .\leavevmode@ifvmode \kern .5em }Podstawowe koncepcje}{13}%
|
||||||
\contentsline {section}{\numberline {4}Modele generatywne stosowane w generowaniu muzyki}{3}%
|
\contentsline {subsection}{\numberline {2.1.1\relax .\leavevmode@ifvmode \kern .5em }Nuta}{13}%
|
||||||
\contentsline {subsection}{\numberline {4.1}Project Magenta}{3}%
|
\contentsline {subsection}{\numberline {2.1.2\relax .\leavevmode@ifvmode \kern .5em }Skala}{13}%
|
||||||
\contentsline {subsection}{\numberline {4.2}MuseGAN}{3}%
|
\contentsline {subsection}{\numberline {2.1.3\relax .\leavevmode@ifvmode \kern .5em }Akord}{13}%
|
||||||
\contentsline {subsection}{\numberline {4.3}VAE-MIDI}{3}%
|
\contentsline {subsection}{\numberline {2.1.4\relax .\leavevmode@ifvmode \kern .5em }Utw\IeC {\'o}r}{14}%
|
||||||
\contentsline {section}{\numberline {5}Budowanie generatora muzyki}{3}%
|
\contentsline {section}{\numberline {2.2\relax .\leavevmode@ifvmode \kern .5em }Reprezentacja muzyki}{14}%
|
||||||
\contentsline {subsection}{\numberline {5.1}Wyodr\IeC {\k e}bnienie danych z plik\IeC {\'o}w MIDI}{3}%
|
\contentsline {subsection}{\numberline {2.2.1\relax .\leavevmode@ifvmode \kern .5em }Zapis klasyczny - pi\IeC {\k e}ciolinia}{14}%
|
||||||
\contentsline {subsection}{\numberline {5.2}Przygotowanie Modelu GAN}{3}%
|
\contentsline {subsection}{\numberline {2.2.2\relax .\leavevmode@ifvmode \kern .5em }Tabulatura}{14}%
|
||||||
\contentsline {subsection}{\numberline {5.3}Proces uczenia, pr\IeC {\'o}bki co kilka epoch\IeC {\'o}w, costloss wykres}{3}%
|
\contentsline {subsection}{\numberline {2.2.3\relax .\leavevmode@ifvmode \kern .5em }Pianoroll}{14}%
|
||||||
\contentsline {subsection}{\numberline {5.4}Pr\IeC {\'o}bki ko\IeC {\'n}cowe, jak\IeC {\k a} muzyk\IeC {\k e} da si\IeC {\k e} z tego wygenerowa\IeC {\'c}}{3}%
|
\contentsline {subsection}{\numberline {2.2.4\relax .\leavevmode@ifvmode \kern .5em }Tekstowa}{15}%
|
||||||
\contentsline {section}{\numberline {6}Podsumowanie}{3}%
|
\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 3\relax .\leavevmode@ifvmode \kern .5em Sieci neuronowe}{17}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.1\relax .\leavevmode@ifvmode \kern .5em }Wst\IeC {\k e}p do sieci neuronowych, definicje wzory itp.}{17}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.2\relax .\leavevmode@ifvmode \kern .5em }Autoencodery}{17}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.3\relax .\leavevmode@ifvmode \kern .5em }LSTM}{17}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.4\relax .\leavevmode@ifvmode \kern .5em }GAN}{18}%
|
||||||
|
\contentsline {chapter}{Budowanie generatora muzyki}{19}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.5\relax .\leavevmode@ifvmode \kern .5em }Przygotowanie danych}{19}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.6\relax .\leavevmode@ifvmode \kern .5em }Architektura sieci neuronowej}{19}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.7\relax .\leavevmode@ifvmode \kern .5em }Proces treningowy}{19}%
|
||||||
|
\contentsline {subsection}{\numberline {3.0.8\relax .\leavevmode@ifvmode \kern .5em }Przyk\IeC {\l }ady wygenerowanej muzyki}{20}%
|
||||||
|
\contentsline {chapter}{Podsumowanie}{21}%
|
||||||
|
\contentsline {chapter}{Bibliografia}{23}%
|
||||||
|
|||||||
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Reference in New Issue
Block a user