%&experiment
\begin{document}
\maketitle

\section{Setup}
At the moment a minimal framework for running experiments has been set-up and
is up and running.

As of now a full album of the death metal band \emph{Cannibal Corpse} has been
annotated. Figure~\ref{fig:bloodstained} shows a segment of the song
\emph{Bloodstained Cement}. From the spectrals it is clearly visible that
during growling the regions around $100$Hz have an increased intensity.

\begin{itemize}
	\item Sox~\footnote{\url{https://sox.sourceforge.net}} is used to convert
		the stereo CD audio to mono $44.1Khz$ waveforms
	\item Using the \texttt{python\_speech\_features}\footnote{\url{%
			https://github.com/jameslyons/python_speech_features}}
		the waveforms are converted to $13$ $MFCC$ cepstrals with the default
		$25ms$ window every $10ms$.
	\item The data is matched with the annotated files using
		\texttt{pympi}~\footnote{\url{https://github.com/dopefishh/pympi}}.
	\item The framework Keras~\footnote{\url{https://keras.io}} is used to
		train models and classify the data
\end{itemize}

\section{Preliminary results}
The simplest models with only one hidden layer already score around $85\%$
accuracy. In the comings week more data will be annotated from different bands
to see the robustness of the models. Moreover, smoothing needs to be applied
because the predictions are very noisy. This is probably due to pauses in
growling. This can easily be smoothed out by not allowing extremely short
growling segments.

\begin{figure}[ht]
	\centering
	\includegraphics[width=.7\linewidth]{cement}
	\caption{A vocal segment of the \emph{Cannibal Corpse} song
		\emph{Bloodstained Cement}}\label{fig:bloodstained}
\end{figure}
\end{document}
