%Methodology

%Experiment(s) (set-up, data, results, discussion)
\section{Data \& Preprocessing}
To run the experiments data has been collected from several \gls{dm} albums.
The exact data used is available in Appendix~\ref{app:data}. The albums are
extracted from the audio CD and converted to a mono channel waveform with the
correct samplerate \emph{SoX}\footnote{\url{http://sox.sourceforge.net/}}.
Every file is annotated using
Praat\cite{boersma_praat_2002} where the utterances are manually aligned to
the audio. Examples of utterances are shown in
Figure~\ref{fig:bloodstained} and Figure~\ref{fig:abominations} where the
waveform, $1-8000$Hz spectrals and annotations are shown. It is clearly visible
that within the genre of death metal there are a different spectral patterns
visible.

\begin{figure}[ht]
	\centering
	\includegraphics[width=.7\linewidth]{cement}
	\caption{A vocal segment of the \emph{Cannibal Corpse} song
		\emph{Bloodstained Cement}}\label{fig:bloodstained}
\end{figure}

\begin{figure}[ht]
	\centering
	\includegraphics[width=.7\linewidth]{abominations}
	\caption{A vocal segment of the \emph{Disgorge} song
		\emph{Enthroned Abominations}}\label{fig:abominations}
\end{figure}

The data is collected from three studio albums. The
first band is called \emph{Cannibal Corpse} and has been producing \gls{dm} for
almost 25 years and have been creating the same type every album. The singer of
\emph{Cannibal Corpse} has a very raspy growls and the lyrics are quite
comprehensible. The vocals produced by \emph{Cannibal Corpse} are bordering
regular shouting. 

The second band is called \emph{Disgorge} and make even more violently sounding
music. The growls of the lead singer sound like a coffee grinder and are more
shallow. In the spectrals it is clearly visible that there are overtones
produced during some parts of the growling. The lyrics are completely
incomprehensible and therefore some parts were not annotated with the actual
lyrics because it was not possible what was being sung.

Lastly a band from Moscow is chosen bearing the name \emph{Who Dies in
Siberian Slush}. This band is a little odd compared to the previous \gls{dm}
bands because they create \gls{dom}. \gls{dom} is characterized by the very
slow tempo and low tuned guitars. The vocalist has a very characteristic growl
and performs in several moscovian bands. This band also stands out because it
uses piano's and synthesizers. The droning synthesizers often operate in the
same frequency as the vocals.

\section{\gls{MFCC} Features}
The waveforms in itself are not very suitable to be used as features due to the
high dimensionality and correlation. Therefore we use the often used
\glspl{MFCC} feature vectors which has shown to be
suitable\cite{rocamora_comparing_2007}. The actual conversion is done using the
\emph{python\_speech\_features}%
\footnote{\url{https://github.com/jameslyons/python_speech_features}} package.

\gls{MFCC} features are nature inspired and built incrementally in several
steps.
\begin{enumerate}
	\item The first step in the process is converting the time representation
		of the signal to a spectral representation using a sliding window with
		overlap. The width of the window and the step size are two important
		parameters in the system. In classical phonetic analysis window sizes
		of $25ms$ with a step of $10ms$ are often chosen because they are small
		enough to only contain subphone entities. Singing for $25ms$ is
		impossible so it is arguable that the window size is very small.
	\item The standard \gls{FT} gives a spectral representation that has
		linearly scaled frequencies. This scale is converted to the \gls{MS}
		using triangular overlapping windows.
	\item
\end{enumerate}


\todo{Explain why MFCC and which parameters}

\section{\gls{ANN} Classifier}
\todo{Spectrals might be enough, no decorrelation}

\section{Model training}

\section{Experiments}

\section{Results}

