\newglossaryentry{dm}{name={Death Metal},
description={is an extreme heavy metal music style with growling vocals and
pounding drums}}
+\newglossaryentry{dom}{name={Doom Metal},
+ description={is an extreme heavy metal music style with growling vocals and
+ pounding drums played very slowly}}
\begin{document}
\frontmatter{}
The exact data used is available in Appendix~\ref{app:data}. The albums are
extracted from the audio CD and converted to a mono channel waveform with the
correct samplerate \emph{SoX}\footnote{\url{http://sox.sourceforge.net/}}.
-When the waveforms are finished they are converted to \glspl{MFCC} vectors
-using the \emph{python\_speech\_features}%
-\footnote{\url{https://github.com/jameslyons/python_speech_features}} package.
-All these steps combined results in thirteen tab separated features per line in
-a file for every source file. Technical info about the processing steps is
-given in the following sections. Every file is annotated using
+Every file is annotated using
Praat\cite{boersma_praat_2002} where the utterances are manually aligned to
the audio. Examples of utterances are shown in
Figure~\ref{fig:bloodstained} and Figure~\ref{fig:abominations} where the
\emph{Enthroned Abominations}}\label{fig:abominations}
\end{figure}
-The data is collected from two\todo{more in the future}\ studio albums. The first
-band is called \emph{Cannibal Corpse} and has been producing \gls{dm} for almost
-25 years and have been creating the same type every album. The singer of
+The data is collected from three studio albums. The
+first band is called \emph{Cannibal Corpse} and has been producing \gls{dm} for
+almost 25 years and have been creating the same type every album. The singer of
\emph{Cannibal Corpse} has a very raspy growls and the lyrics are quite
-comprehensible. The second band is called \emph{Disgorge} and make even more
-violent music. The growls of the lead singer sound more like a coffee grinder
-and are more shallow. The lyrics are completely incomprehensible and therefore
-some parts are not annotated with lyrics because it was too difficult to hear
-what was being sung.
-
-\section{Methods}
-\todo{To remove in final thesis}
-The initial planning is still up to date. About one and a half album has been
-annotated and a framework for setting up experiments has been created.
-Moreover, the first exploratory experiments are already been executed and
-promising. In April the experimental dataset will be expanded and I will try to
-mimic some of the experiments done in the literature to see whether it performs
-similar on Death Metal
-\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}
-\end{table}
-
-\section{Features}
-
+comprehensible. The vocals produced by \emph{Cannibal Corpse} are bordering
+regular shouting.
+
+The second band is called \emph{Disgorge} and make even more violently sounding
+music. The growls of the lead singer sound like a coffee grinder and are more
+shallow. In the spectrals it is clearly visible that there are overtones
+produced during some parts of the growling. The lyrics are completely
+incomprehensible and therefore some parts were not annotated with the actual
+lyrics because it was not possible what was being sung.
+
+Lastly a band from Moscow is chosen bearing the name \emph{Who Dies in
+Siberian Slush}. This band is a little odd compared to the previous \gls{dm}
+bands because they create \gls{dom}. \gls{dom} is characterized by the very
+slow tempo and low tuned guitars. The vocalist has a very characteristic growl
+and performs in several moscovian bands. This band also stands out because it
+uses piano's and synthesizers. The droning synthesizers often operate in the
+same frequency as the vocals.
+
+\section{\gls{MFCC} Features}
+The waveforms are converted to \glspl{MFCC} feature vectors using the
+\emph{python\_speech\_features}%
+\footnote{\url{https://github.com/jameslyons/python_speech_features}} package.
+All these steps combined results in thirteen tab separated features per line in
+a file for every source file. Technical info about the processing steps is
+given in the following sections.
\todo{Explain why MFCC and which parameters}
+
+\section{\gls{ANN} Classifier}
\todo{Spectrals might be enough, no decorrelation}
+\section{Model training}
+
\section{Experiments}
\section{Results}
19 & Disgorge & Parallels of Infinite Torture & Parallels of Infinite Torture & 05:03.33\\
20 & Disgorge & Parallels of Infinite Torture & Asphyxiation of Thee Oppressed & 05:42.37\\
21 & Disgorge & Parallels of Infinite Torture & Ominous Sigils of Ungodly Ruin & 04:59.15\\
+ 22 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & Leave Me & 06:35.60\\
+ 23 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & The Woman We Are Looking For & 06:53.63\\
+ 24 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & M\"obius Ring & 07:20.56\\
+ 25 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & Interlude & 04:26.49\\
+ 26 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & Завещание Гумилёва & 08:46.76\\
+ 27 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & An Old Road Through The Snow & 02:31.56\\
+ 28 & Who Dies In Siberian Slush & Bitterness Of The Years That Are Lost & Bitterness Of The Years That Are Lost & 09:10.49\\
+ \midrule
+ & & & Total: & 02:13:40\\
\bottomrule
\end{tabular}
\caption{Songs used in the experiments}
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