%&asr
\usepackage[toc,nonumberlist,acronyms]{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}}}

\begin{document}
\frontmatter{}

\maketitleru[
	course={(Automatic) Speech Recognition},
	institute={Radboud University Nijmegen},
	authorstext={Author:},
	pagenr=1]
\listoftodos[Todo]

\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}.
%


%Introduction, leading to a clearly defined research question
\chapter{Introduction}
\input{intro.tex}

\chapter{Methods}
\input{methods.tex}

\chapter{Conclusion \& Discussion}
\input{conclusion.tex}

%(Appendices)
\appendix
\input{appendices.tex}

\newpage
%Glossaries
\glsaddall{}
\begingroup
\let\clearpage\relax
\let\cleardoublepage\relax
\printglossaries{}
\endgroup

\bibliographystyle{ieeetr}
\bibliography{asr}
\end{document}
