+\section{Conclusion}
+This study shows that existing techniques for singing-voice detection
+designed for regular singing-voices also work on \gls{dm} and \gls{dom} that
+contain extreme singing styles like grunting. With a standard \gls{ANN}
+classifier using \gls{MFCC} features a performance of $85\%$ can be achieved
+which is similar to the same techniques used on regular singing. This means
+that it might also be suitable as a pre-processing step for lyrics forced
+alignment. Moreover, the \emph{singer}-voice recognition experiments scored
+similarly.
+
+To determine whether the model generalizes, alien data has been offered to the
+model to see how it performs. It was shown that for similar singing styles the
+models perform similar. The alien data offered containing different singing
+styles, atmospheric noise and accompaniment is classified worse.
+
+From the results we can conclude that the model generalizes well over the
+trainings set, even with a small number of hidden nodes. The models with 3 or 5
+hidden nodes score a little worse than their bigger brothers but there is
+hardly any difference between the performance of a model with 8 or 13 nodes.
+Moreover, contrary than expected the window size does not seem to be doing much
+in the performance.