elaborate on genre recoginition

[asr1617.git] / results.tex

diff --git a/results.tex b/results.tex
index bb05b6b..57f6779 100644 (file)
--- a/results.tex
+++ b/results.tex
@@ -1,12 +1,9 @@
 \section{\emph{Singing}-voice detection}
 Table~\ref{tbl:singing} shows the results for the singing-voice detection. The
-performance is given by the accuracy (and loss). The accuracy is the percentage
-of correctly classified samples.
-
-Figure~\ref{fig:bclass} shows an example of a segment of a song with the
-classifier plotted underneath. For this illustration the $13$ node model is
-used with a analysis window size and step of $40$ and $100ms$ respectively. The
-output is smoothed using a hanning window.
+performance is given by the fraction of correctly classified samples
+(accuracy). The rows represent the count of hidden nodes, the columns represent
+the analysis window step size and the analysis window length in the \gls{MFCC}
+extraction.
 
 \begin{table}[H]
        \centering
@@ -26,6 +23,14 @@ output is smoothed using a hanning window.
        \label{tbl:singing}
 \end{table}
 
+Figure~\ref{fig:bclass} shows an example of a segment of a song with the
+classifier plotted underneath. For this illustration the $13$ node model is
+used with a analysis window size and step of $40$ and $100$ respectively. The
+output is smoothed using a hanning window. This figure shows that the model
+focusses on the frequencies around $300Hz$ which contain the growling. When
+there is a little silence in between the growls the classifier immediately
+drops. This phenomenon is visible throughout the songs.
+
 \begin{figure}[H]
        \centering
        \includegraphics[width=1\linewidth]{bclass}