diff --git a/docs/document.aux b/docs/document.aux index d9aa9c1..1e1f80d 100644 --- a/docs/document.aux +++ b/docs/document.aux @@ -113,12 +113,18 @@ \@writefile{toc}{\contentsline {subsection}{\numberline {3.5.1\relax .\leavevmode@ifvmode \kern .5em }Oprogramowanie}{52}\protected@file@percent } \@writefile{toc}{\contentsline {subsection}{\numberline {3.5.2\relax .\leavevmode@ifvmode \kern .5em }Zbi\IeC {\'o}r danych}{52}\protected@file@percent } \@writefile{toc}{\contentsline {subsection}{\numberline {3.5.3\relax .\leavevmode@ifvmode \kern .5em }Wydobycie danych}{52}\protected@file@percent } -\@writefile{toc}{\contentsline {section}{\numberline {3.6\relax .\leavevmode@ifvmode \kern .5em }Trenowanie modelu}{53}\protected@file@percent } -\@writefile{toc}{\contentsline {section}{\numberline {3.7\relax .\leavevmode@ifvmode \kern .5em }Generowanie muzyki przy pomocy wytrenowanych modeli}{54}\protected@file@percent } +\@writefile{toc}{\contentsline {subsection}{\numberline {3.5.4\relax .\leavevmode@ifvmode \kern .5em }Trenowanie modelu}{53}\protected@file@percent } +\@writefile{toc}{\contentsline {subsection}{\numberline {3.5.5\relax .\leavevmode@ifvmode \kern .5em }Generowanie muzyki przy pomocy wytrenowanych modeli}{54}\protected@file@percent } \newlabel{fig:losses}{{3.3}{55}} -\@writefile{toc}{\contentsline {section}{\numberline {3.8\relax .\leavevmode@ifvmode \kern .5em }Wyniki}{56}\protected@file@percent } -\@writefile{toc}{\contentsline {section}{\numberline {3.9\relax .\leavevmode@ifvmode \kern .5em }Wnioski}{56}\protected@file@percent } -\@writefile{toc}{\contentsline {chapter}{Rozdzia\PlPrIeC {\l }\ 4\relax .\leavevmode@ifvmode \kern .5em Podsumowanie}{57}\protected@file@percent } +\@writefile{toc}{\contentsline {subsection}{\numberline {3.5.6\relax .\leavevmode@ifvmode \kern .5em }Wyniki}{56}\protected@file@percent } +\newlabel{fig:score1}{{3.4}{57}} +\newlabel{fig:score10}{{3.5}{57}} +\newlabel{fig:score25}{{3.6}{58}} +\newlabel{fig:score50}{{3.7}{58}} +\newlabel{fig:score75}{{3.8}{59}} +\newlabel{fig:score100}{{3.9}{59}} +\newlabel{fig:score150}{{3.10}{60}} +\@writefile{toc}{\contentsline {chapter}{Podsumowanie}{61}\protected@file@percent } \@writefile{lof}{\addvspace {10\p@ }} \@writefile{lot}{\addvspace {10\p@ }} \bibcite{survay}{1} @@ -131,6 +137,6 @@ \bibcite{tempos}{8} \bibcite{deep_learning_2}{9} \bibcite{seq2seq-keras}{10} -\@writefile{toc}{\contentsline {chapter}{Bibliografia}{59}\protected@file@percent } +\@writefile{toc}{\contentsline {chapter}{Bibliografia}{63}\protected@file@percent } \@writefile{lof}{\addvspace {10\p@ }} \@writefile{lot}{\addvspace {10\p@ }} diff --git a/docs/document.fdb_latexmk b/docs/document.fdb_latexmk index 3b5e4a5..a83bfad 100644 --- a/docs/document.fdb_latexmk +++ b/docs/document.fdb_latexmk @@ -1,10 +1,10 @@ # Fdb version 3 -["makeindex document.idx"] 1592389906 "document.idx" "document.ind" "document" 1592390153 - "document.idx" 1592390127 0 d41d8cd98f00b204e9800998ecf8427e "" +["makeindex document.idx"] 1592392720 "document.idx" "document.ind" "document" 1592392736 + "document.idx" 1592392724 0 d41d8cd98f00b204e9800998ecf8427e "" (generated) - "document.ilg" "document.ind" -["pdflatex"] 1592390149 "j:/_MAGISTERKA/praca-magisterska/docs/document.tex" "j:/_MAGISTERKA/praca-magisterska/docs/document.pdf" "document" 1592390153 + "document.ilg" +["pdflatex"] 1592392729 "j:/_MAGISTERKA/praca-magisterska/docs/document.tex" "j:/_MAGISTERKA/praca-magisterska/docs/document.pdf" "document" 1592392736 "c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plit.enc" 1550343089 1946 62ba825cda1ff16dbaa60d53ac60525a "" "c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plme.enc" 1550343089 3126 59f33ff3e396436ebc36f4e7b444d5c4 "" "c:/software/latex/texmf-dist/fonts/enc/dvips/pl/plmi.enc" 1550343089 2005 f094775651a1386335c158fb632529f1 "" @@ -196,10 +196,10 @@ "c:/software/latex/texmf-var/web2c/pdftex/pdflatex.fmt" 1550344823 4224189 05b80a2fc98956d1ad757099b6714631 "" "c:/software/latex/texmf.cnf" 1550344763 715 839ef4b14d931ce86420174d3a223d3e "" "chapter-style.sty" 1560952449 376 87871882de090607c810d89fd5ae18d9 "" - 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PDF statistics: - 332 PDF objects out of 1000 (max. 8388607) - 212 compressed objects within 3 object streams + 352 PDF objects out of 1000 (max. 8388607) + 221 compressed objects within 3 object streams 0 named destinations out of 1000 (max. 500000) - 103 words of extra memory for PDF output out of 10000 (max. 10000000) + 138 words of extra memory for PDF output out of 10000 (max. 10000000) diff --git a/docs/document.pdf b/docs/document.pdf index 7a6ff9c..087ae6a 100644 Binary files a/docs/document.pdf and b/docs/document.pdf differ diff --git a/docs/document.synctex.gz b/docs/document.synctex.gz index 7a6b3aa..795d2be 100644 Binary files a/docs/document.synctex.gz and b/docs/document.synctex.gz differ diff --git a/docs/document.tex b/docs/document.tex index 811a4df..2e3ba80 100644 --- a/docs/document.tex +++ b/docs/document.tex @@ -1060,7 +1060,7 @@ Model sequence-to-sequence, jest to model skÅ‚adajÄ…cy siÄ™ z dwóch mniejszych \subsubsection{Zdefiniowanie warstw enkodera} -Zadaniem enkodera jest wydobycie z przetwarzanej sekwekcji kontekstu, skompresowanej informacji o danych. W tym celu zastosowana zostaÅ‚a jedna warstwa wejÅ›ciowa o rozmiarze sÅ‚ownika wejÅ›ciowego, oraz warstwa LSTM. Definiowane warstwy sÄ… atrybutami caÅ‚ej klasy modelu, dlatego w prezentowanym kodzie wystÄ™pujÄ… przedrostek \pyth{self.}. +Zadaniem enkodera jest wydobycie z przetwarzanej sekwekcji kontekstu, skompresowanej informacji o danych. W tym celu zastosowana zostaÅ‚a jedna warstwa wejÅ›ciowa o rozmiarze sÅ‚ownika wejÅ›ciowego, oraz warstwa LSTM. Definiowane warstwy sÄ… atrybutami caÅ‚ej klasy modelu, dlatego w prezentowanym kodzie wystÄ™puje przedrostek \pyth{self.}. \begin{python} self.encoder_inputs = @@ -1432,7 +1432,7 @@ Po tym etapie zostaÅ‚y utworzony pliki zawierajÄ…ce oczyszczone pary sekwencji d Na podstawie takiego zbioru danych, w nastepnym kroku zostanÄ… wytrenowane czrety sieci neuronowe, po jednej dla każdego intrumentu. -\section{Trenowanie modelu} +\subsection{Trenowanie modelu} UzywajÄ…c skypt \pyth{train.py} możemy w prosty sposób wytrenować wszystkie modele, a wagi zapiszÄ… i bÄ™dzie można je wykorzystać w celu generowania, lub w celu dalszego uczenia. @@ -1472,7 +1472,7 @@ Na potrzeby badaÅ„ trenowaÅ‚em i generowaÅ‚em klipy muzyczne dla epok 1, 10, 25, \label{fig:losses} \end{figure} -\section{Generowanie muzyki przy pomocy wytrenowanych modeli} +\subsection{Generowanie muzyki przy pomocy wytrenowanych modeli} Gdy zdefiniowane modele zostanÄ… wytrenowane możemy wykorzystać skrypt \pyth{generate.py}, wtedy generujÄ…ca sieć neuonowa zostanie zasilona losowym wekorem aby wygenerować partiÄ™. W tym przykÅ‚adzie gitary a wygenerowana partia posÅ‚uży jako dane wejÅ›ciowe na pozostaÅ‚ych modeli. Ostatecznie otrzymane sekwencje zostanÄ… skompilowane do pliku MIDI. W tym momencie zostajÄ… wykorzystane informacje o programach dla każdego z intrmentów, a tempo utworu domyÅ›lnie ustawione jest na 120 BPM. Możemy również zdecydować, czy zasilenie dekodera modelu generujÄ…cego odbÄ™dzie siÄ™ za pomocÄ… losowej sekwencji elementów ze sÅ‚ownika (\textit{from\textunderscore seq}), czy losowy wektor zasili bezpoÅ›rednio stany wewnÄ™trzne dekodera $h$ i $c$ (\textit{from\textunderscore state}). @@ -1529,14 +1529,63 @@ generated_midi.save(save_path) W ten sposób pomyÅ›lnie zostaÅ‚y wygenerowane fragmenty muzyczne przy pomocy gÅ‚Ä™bokiego uczenia. -\section{Wyniki} +\subsection{Wyniki} +PrzedstawiÄ™ wybrane fragmenty wygenerowanych utworów w zapisie nutowym dla kolejnych etapów uczenia. Krótko scharakteryzujÄ™ każdy fragment oraz opowiem jakie ulepszenia pojawiaÅ‚y siÄ™ wraz z postÄ™pem treningu. + +\begin{figure}[!htb] + \centering + \includegraphics[width=\linewidth]{images/score_1.png} + \caption[]{Wygenerowany fragment po jednej epoce.} + \label{fig:score1} +\end{figure} + +\begin{figure}[!htb] + \centering + \includegraphics[width=\linewidth]{images/score_10.png} + \caption[]{Wygenerowany fragment po dziesiÄ™ciu epokach.} + \label{fig:score10} +\end{figure} + +\begin{figure}[!htb] + \centering + \includegraphics[width=\linewidth]{images/score_25.png} + \caption[]{Wygenerowany fragment po dwudziestu piÄ™ciu epokach.} + \label{fig:score25} +\end{figure} + +\begin{figure}[!htb] + \centering + \includegraphics[width=\linewidth]{images/score_50.png} + \caption[]{Wygenerowany fragment po pięćdziesiÄ™ciu epokach.} + \label{fig:score50} +\end{figure} + +\begin{figure}[!htb] + \centering + \includegraphics[width=\linewidth]{images/score_75.png} + \caption[]{Wygenerowany fragment po siedemdziesiÄ™ciu piÄ™ciu epokach} + \label{fig:score75} +\end{figure} + +\begin{figure}[!htb] + \centering + \includegraphics[width=\linewidth]{images/score_100.png} + \caption[]{Wygenerowany fragment po stu epokach} + \label{fig:score100} +\end{figure} + +\begin{figure}[!htb] + \centering + \includegraphics[width=\linewidth]{images/score_150.png} + \caption[]{Wygenerowany fragment po stu pięćdziesiÄ™ciu epokach} + \label{fig:score150} +\end{figure} -\section{Wnioski} +\chapter*{Podsumowanie} -\chapter{Podsumowanie} Ostateczne wnioski, czy muzyka generowana komputerowa da siÄ™ lubić? Czy to pozytywnie wpÅ‚ynie na przemysÅ‚ muzyczny? Tak i nie. Może sÅ‚użyć jako inspiracja dla muzyków, proces wspierajÄ…cy. Z drugiej strony może obniży koszty produkowania muzyki pop, która i tak jest już bardzo powtarzalna. 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