\section{\emph{Singing}-voice detection}
Table~\ref{tbl:singing} shows the results for the singing-voice detection. The
performance is given by the fraction of correctly classified samples
(accuracy). The rows represent the count of hidden nodes, the columns represent
the analysis window step size and the analysis window length in the \gls{MFCC}
extraction.

\begin{table}[H]
	\centering
	\begin{tabular}{rrccc}
		\toprule
		   & & \multicolumn{3}{c}{Parameters (step/length)}\\
		   & & 10/25 & 40/100 & 80/200\\
		\midrule
		\multirow{4}{*}{Hidden Nodes}
		 & 3 & 0.86 (0.34) & 0.87 (0.32) & 0.85 (0.35)\\
		 & 5 & 0.87 (0.31) & 0.88 (0.30) & 0.87 (0.32)\\
		 & 8 & 0.88 (0.30) & 0.88 (0.31) & 0.88 (0.29)\\
		 & 13 & 0.89 (0.28) & 0.89 (0.29) & 0.88 (0.30)\\
		\bottomrule
	\end{tabular}
	\caption{Binary classification results (accuracy (loss))}%
	\label{tbl:singing}
\end{table}

Figure~\ref{fig:bclass} shows an example of a segment of a song with the
classifier plotted underneath. For this illustration the $13$ node model is
used with a analysis window size and step of $40$ and $100$ respectively. The
output is smoothed using a hanning window. This figure shows that the model
focusses on the frequencies around $300Hz$ which contain the growling. When
there is a little silence in between the growls the classifier immediately
drops. This phenomenon is visible throughout the songs.

\begin{figure}[H]
	\centering
	\includegraphics[width=1\linewidth]{bclass}
	\caption{Plotting the classifier under the audio signal}\label{fig:bclass}
\end{figure}

\section{\emph{Singer}-voice detection}
Table~\ref{tbl:singer} shows the results for the singer-voice detection. The
same metrics are used as in \emph{Singing}-voice detection.

\begin{table}[H]
	\centering
	\begin{tabular}{rrccc}
		\toprule
		   & & \multicolumn{3}{c}{Parameters (step/length)}\\
		   & & 10/25 & 40/100 & 80/200\\
		\midrule
		\multirow{4}{*}{Hidden Nodes}
		 & 3 & 0.83 (0.48) & 0.82 (0.48) & 0.82 (0.48)\\
		 & 5 & 0.85 (0.43) & 0.84 (0.44) & 0.84 (0.44)\\
		 & 8 & 0.86 (0.41) & 0.86 (0.39) & 0.86 (0.40)\\
		 & 13 & 0.87 (0.37) & 0.87 (0.38) & 0.86 (0.39)\\
		\bottomrule
	\end{tabular}
	\caption{Multiclass classification results (accuracy
		(loss))}\label{tbl:singer}
\end{table}

\section{Alien data}
To test the generalizability of the models the system is tested on alien data.
The data was retrieved from the album \emph{The Desperation} by \emph{Godless
Truth}. \emph{Godless Truth} is a so called old-school \gls{dm} band that has
very raspy vocals and the vocals are very up front in the mastering. This means
that the vocals are very prevalent in the recording and therefore no difficulty
is expected for the classifier. Figure~\ref{fig:alien1} shows that indeed the
classifier scores very accurately. Note that the spectogram settings have been
adapted a little bit to make the picture more clear. The spectogram shows the
frequency range from $0$ to $3000Hz$.

\begin{figure}[H]
	\centering
	\includegraphics[width=.7\linewidth]{alien1}.
	\caption{Plotting the classifier with alien data containing familiar vocal
	styles}\label{fig:alien1}
\end{figure}

To really test the limits, a song from the highly atmospheric doom metal band
called \emph{Catacombs} has been tested on the system. The album \emph{Echoes
Through the Catacombs} is an album that has a lot of synthesizers, heavy
droning guitars and bass lines. The vocals are not mixed in a way that makes
them stand out. The models have never seen trainingsdata that is even remotely
similar to this type of metal. Figure~\ref{fig:alien2} shows a segment of the
data. It is visible that the classifier can not distinguish singing from non
singing.

\begin{figure}[H]
	\centering
	\includegraphics[width=.7\linewidth]{alien2}.
	\caption{Plotting the classifier with alien data containing strange vocal
	styles}\label{fig:alien2}
\end{figure}
