conclusie

diff --git a/conclusion.tex b/conclusion.tex
index 6bd8087..700b5e8 100644 (file)
--- a/conclusion.tex
+++ b/conclusion.tex
@@ -1,22 +1,26 @@
 \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.
+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.
 
-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.
+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 less good.
+
+From the results we can 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: }
+\paragraph{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