%&asr
-\usepackage[toc,nonumberlist,acronyms]{glossaries}
+\usepackage[nonumberlist]{glossaries}
\makeglossaries%
-\newacronym{ANN}{ANN}{Artificial Neural Network}
-\newacronym{DCT}{DCT}{Discrete Cosine Transform}
-\newacronym{DHMM}{DHMM}{Duration-explicit \acrlong{HMM}}
-\newacronym{FA}{FA}{Forced alignment}
-\newacronym{GMM}{GMM}{Gaussian Mixture Models}
-\newacronym{HMM}{HMM}{Hidden Markov Model}
-\newacronym{HTK}{HTK}{\acrlong{HMM} Toolkit}
-\newacronym{IFPI}{IFPI}{International Federation of the Phonographic Industry}
-\newacronym{LPCC}{LPCC}{\acrlong{LPC} derivec cepstrum}
-\newacronym{LPC}{LPC}{Linear Prediction Coefficients}
-\newacronym{MFCC}{MFCC}{\acrlong{MFC} coefficient}
-\newacronym{MFC}{MFC}{Mel-frequency cepstrum}
-\newacronym{MLP}{MLP}{Multi-layer Perceptron}
-\newacronym{PLP}{PLP}{Perceptual Linear Prediction}
-\newacronym{PPF}{PPF}{Posterior Probability Features}
-\newacronym{ZCR}{ZCR}{Zero-crossing Rate}
-\newglossaryentry{dm}{name={Death Metal},
- description={is an extreme heavy metal music style with growling vocals and
- pounding drums}}
-\newglossaryentry{dom}{name={Doom Metal},
- description={is an extreme heavy metal music style with growling vocals and
- pounding drums played very slowly}}
-\newglossaryentry{FT}{name={Fourier Transform},
- description={is a technique of converting a time representation signal to a
- frequency representation}}
-\newglossaryentry{MS}{name={Mel-Scale},
- description={is a human ear inspired scale for spectral signals.}}
-\newglossaryentry{Viterbi}{name={Viterbi},
- description={is a dynamic programming algorithm for finding the most likely
- sequence of hidden states in a \gls{HMM}}}
+\input{glossaries}
\begin{document}
\frontmatter{}
-\maketitleru[
- course={(Automatic) Speech Recognition},
- institute={Radboud University Nijmegen},
- authorstext={Author:},
- righttextheader={Supervisor:},
- righttext={Louis ten Bosch},
- pagenr=1]
-\listoftodos[Todo]
-
+\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
-\mainmatter{}
-%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}.
-%
-%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}.
-%
+\glsaddall{}
+\mainmatter{}
-%Introduction, leading to a clearly defined research question
\chapter{Introduction}
-\input{intro.tex}
+\input{intro}
\chapter{Methods}
-\input{methods.tex}
+\input{methods}
+
+\chapter{Results}
+\input{results}
-\chapter{Conclusion \& Discussion}
-\input{conclusion.tex}
+\chapter{Discussion \& Conclusion}
+\input{conclusion}
%(Appendices)
\appendix
-\input{appendices.tex}
+\input{appendices}
-\newpage
-%Glossaries
-\glsaddall{}
-\begingroup
-\let\clearpage\relax
-\let\cleardoublepage\relax
-\printglossaries{}
-\endgroup
+\addcontentsline{toc}{chapter}{Glossaries \& Acronyms}
+\printglossaries%
\bibliographystyle{ieeetr}
\bibliography{asr}