\section{Conclusion \& Future Research}
This research shows that existing techniques for singing-voice detection
designed for regular singing voices also work respectably on 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 on regular singing. This means that it might be suitable as a
pre-processing step for lyrics forced alignment.

Future interesting research includes doing the actual forced alignment. This
probably requires entirely different models. The models used for real speech
are probably not suitable because the acoustic properties of a regular singing
voice is very different from a growling voice, let alone speech.

Secondly, it would be interesting if a model could be trained that could
discriminate a singing voice for all styles of singing including growling.
Moreover, it is possible to investigate the performance of detecting growling
on regular singing-voice trained models and the other way around.

%Discussion section
\section{Discussion}
The dataset used is not very big. Only three albums are annotated and used
as training data. The albums chosen do represent the ends of the spectrum and
therefore the resulting model can be very general. However, it could also mean
that the model is able to recognize three islands in the entire space of
grunting. This does not seem the case since the results show that totally alien
data also has a good performance.

The model clearly has trouble with pauses between singing.

\emph{Singing}-voice detection and \emph{singer}-voice Singing-voice detection
can be seen as a crude way of genre-discrimination. Therefore it be
generalizable to extensive genre recognition
might.
