\section{Conclusion}
This study 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. The model performs pretty well
on alien data that uses similar singing techniques as the training set.
However, the model does not cope very well with different singing techniques or
with data that contains a lot of atmospheric noise and accompaniment.

From the results we conclude that the model generalizes well over the trainings
set, even with little 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.

\section{Future research}
\paragraph{Forced aligment: }
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 are very different from those of a growling voice, let alone
speech.

\paragraph{Generalization: }
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.

\paragraph{Decorrelation }
Another interesting research continuation would be to investigate whether the
decorrelation step of the feature extraction is necessary. This transformation
might be inefficient or unnatural. The first layer of weights in the model
could be seen as a first processing step. If another layer is added that layer
could take over the role of the decorrelating. The downside of this is that
training the model is tougher because there are a many more weights to train.

\paragraph{Genre detection: }
\emph{Singing}-voice detection and \emph{singer}-voice can be seen as a crude
way of genre-detection. Therefore it might be interesting to figure out whether
this is generalizable to general genre recognition. This requires more data
from different genres to be added to the dataset and the models to be
retrained.

\paragraph{\glspl{HMM}: }
A lot of similar research on singing-voice detection uses \glspl{HMM} and
existing phone models. It would be interesting to try the same approach on
extreme singing styles to see whether the phone models can say anything about a
growling voice.

%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 almost all
alien data also has a good performance. However, the data has been picked to
represent the edges of the spectrum. While testing \emph{Catacombs} it seemed
that this was not the case since the performance was very poor. Adding
\emph{Catacombs} or a similar style to the training set can probably overcome
this limitation.
