elaborate on the conclusion

[asr1617.git] / methods.tex

diff --git a/methods.tex b/methods.tex
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+++ b/methods.tex
@@ -253,3 +253,33 @@ batch size of $32$.
 \end{table}
 
 \subsection{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=.6\linewidth]{alien1}.
+       \caption{Plotting the classifier under similar alien data}\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. Here it is clearly visible that the classifier can not distinguish
+singing from non singing.
+
+\begin{figure}[H]
+       \centering
+       \includegraphics[width=.6\linewidth]{alien1}.
+       \caption{Plotting the classifier under different alien data}\label{fig:alien2}
+\end{figure}