%&asr
\usepackage[nonumberlist,acronyms]{glossaries}
\makeglossaries%
\newacronym{HMM}{HMM}{Hidden Markov Model}
\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}}

\begin{document}
%Titlepage
\maketitleru[
	course={(Automatic) Speech Recognition},
	institute={Radboud University Nijmegen},
	authorstext={Author:}]
\listoftodos[Todo]

\tableofcontents

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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.

t\cite{dzhambazov_automatic_2014}
t\cite{dzhambazov_automatic_2016}
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}

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