finalize proposal

diff --git a/proposal.pre b/proposal.pre
index 5f67969..c9cfd70 100644 (file)
--- a/proposal.pre
+++ b/proposal.pre
@@ -4,6 +4,7 @@
 
 \usepackage{geometry}                % Papersize
 \usepackage{hyperref}                % Hyperlinks
+\usepackage{booktabs}                % Nice tables
 
 \urlstyle{same}
 \hypersetup{%

diff --git a/proposal.tex b/proposal.tex
index a5125d0..a2134b5 100644 (file)
--- a/proposal.tex
+++ b/proposal.tex
@@ -12,7 +12,9 @@ tackled using myriads of different approaches such as HMMs with different
 acoustic model types but also machine learned feature sets. I would like to
 explore how HMM based techniques perform on extreme heavy metal styles to see
 how well it can detect growling and how classifier might be adapted to perform
-better.
+better. Initially the classifier will be a binary classifier that classifies
+growling and non-growling. Later on classes might be added such as and musical
+genres.
 
 Singing voice detection is often used as a preprocessing step for song lyrics
 forced alignment. If the time permits I would like to explore forced alignment
@@ -20,6 +22,44 @@ using existing phone models on extreme heavy metal styles. Features probably
 need to be changed to improve performance since growling is very different from
 regular singing and speaking.
 
-The data for this will be coming from my personal collection audio CDs.
+The data for this will be coming from my personal collection audio CDs. For the
+singing voice detection the data of one band can be used and even a test set
+can be held out since the band made over 15 full studio albums each with a
+running time between 30 and 60 minutes. The segmentation for the trainingsdata
+can be done by hand. Later, for the lyrics alignment, the labels for the
+segments can be found online on song lyric websites. Validation of the
+alignment is a bit tricky however since there is no golden standard but my own.
+
+\paragraph{Planning}\strut\\
+This results in the following rough outline divided on a month by month basis
+shown in Table~\ref{tbl:outline}.
+Possible pitfalls can arise in preparing the data since that requires
+segmentation. It is expected to take around twice the playing time but that
+might be an overestimation.
+
+\begin{table}[ht]
+       \centering
+       \begin{tabular}{cll}
+               \toprule
+               Month & Description\\
+               \midrule
+               March
+                       & Preparing the data\\
+                       & Preparing an experiment platform\\
+                       & Literature research\\
+               April
+                       & Running the experiments\\
+                       & Fiddle with parameters\\
+                       & Explore the possibilities for forced alignment\\
+               May
+                       & Write up the thesis\\
+                       & Possibly do forced alignment\\
+               June
+                       & Finish up thesis\\
+                       & Wrap up\\
+               \bottomrule
+       \end{tabular}
+       \caption{Outline}\label{tbl:outline}
+\end{table}
 
 \end{document}