true final

[asr1617.git] / asr.tex

diff --git a/asr.tex b/asr.tex
index 87aae4e..608eb1e 100644 (file)
--- a/asr.tex
+++ b/asr.tex
@@ -1,67 +1,52 @@
 %&asr
-\usepackage[nonumberlist,acronyms]{glossaries}
+\usepackage[nonumberlist]{glossaries}
 \makeglossaries%
-\newacronym{HMM}{HMM}{Hidden Markov Model}
-\newacronym{GMM}{GMM}{Gaussian Mixture Models}
-\newacronym{DHMM}{DHMM}{Duration-explicit \acrlong{HMM}}
-\newacronym{HTK}{HTK}{\acrlong{HMM} Toolkit}
-\newacronym{FA}{FA}{Forced alignment}
-\newacronym{MFC}{MFC}{Mel-frequency cepstrum}
-\newacronym{MFCC}{MFCC}{\acrlong{MFC} coefficient}
-%\newglossaryentry{mTask}{name=mTask,
-%      description={is an abstraction for \glspl{Task} living on \acrshort{IoT} devices}}
+\input{glossaries}
 
 \begin{document}
-%Titlepage
-\maketitleru[
-       course={(Automatic) Speech Recognition},
-       institute={Radboud University Nijmegen},
-       authorstext={Author:}]
-\listoftodos[Todo]
-
+\frontmatter{}
+
+\input{titlepage}
+
+%Abstract
+\addcontentsline{toc}{chapter}{Abstract}
+\chapter*{\centering Abstract}
+\begin{quotation}
+       \centering\noindent
+       \input{abstract}
+\end{quotation}
+
+% Acknowledgements
+\addcontentsline{toc}{chapter}{Acknowledgements}
+\chapter*{\centering Acknowledgements}
+\begin{quotation}
+       \centering\it\noindent
+       \input{acknowledgements}
+\end{quotation}
 \tableofcontents
 
-%Glossaries
 \glsaddall{}
-\printglossaries%
 
-Berenzweig and Ellis use acoustic classifiers from speech recognition as a
-detector for singing lines.  They achive 80\% accuracy for forty 15 second
-exerpts. They mention people that wrote signal features that discriminate
-between speech and music. Neural net
-\glspl{HMM}~\cite{berenzweig_locating_2001}.
+\mainmatter{}
+
+\chapter{Introduction}
+\input{intro}
 
-In 2014 Dzhambazov et al.\ applied state of the art segmentation methods to
-polyphonic turkish music, this might be interesting to use for heavy metal.
-They mention Fujihara(2011) to have a similar \gls{FA} system. This method uses
-phone level segmentation, first 12 \gls{MFCC}s. They first do vocal/non-vocal
-detection, then melody extraction, then alignment. They compare results with
-Mesaros \& Virtanen, 2008~\cite{dzhambazov_automatic_2014}. Later they
-specialize in long syllables in a capella. They use \glspl{DHMM} with
-\glspl{GMM} and show that adding knowledge increases alignment (bejing opera
-has long syllables)~\cite{dzhambazov_automatic_2016}.
+\chapter{Methods}
+\input{methods}
 
-t\cite{fujihara_automatic_2006}
-t\cite{fujihara_lyricsynchronizer:_2011}
-t\cite{fujihara_three_2008}
-t\cite{mauch_integrating_2012}
-t\cite{mesaros_adaptation_2009}
-t\cite{mesaros_automatic_2008}
-t\cite{mesaros_automatic_2010}
-t\cite{muller_multimodal_2012}
-t\cite{pedone_phoneme-level_2011}
-t\cite{yang_machine_2012}
+\chapter{Results}
+\input{results}
 
+\chapter{Discussion \& Conclusion}
+\input{conclusion}
 
-%Introduction, leading to a clearly defined research question
-%Literature overview / related work
-%Methodology
-%Experiment(s) (set-up, data, results, discussion)
-%Discussion section
-%Conclusion section
-%Acknowledgements
-%Statement on authors' contributions
 %(Appendices)
+\appendix
+\input{appendices}
+
+\addcontentsline{toc}{chapter}{Glossaries \& Acronyms}
+\printglossaries%
 
 \bibliographystyle{ieeetr}
 \bibliography{asr}