techniques for segmenting instrumental and vocal parts of a song and
apply forced alignment or even lyrics recognition on the audio file.
+Such techniques are heavily researched and working systems have been created.
+However, these techniques are designed to detect a clean singing voice. Extreme
+genres such as \gls{dm} are using more extreme vocal techniques such as
+grunting or growling. It must be noted that grunting is not a technique only
+used in extreme metal styles. Similar or equal techniques have been used in
+\emph{Beijing opera}, Japanese \emph{Noh} and but also more western styles like
+jazz singing by Louis Armstrong~\cite{sakakibara_growl_2004}. It might even be
+traced back to viking times. An arab merchant wrote in the tenth
+century~\cite{friis_vikings_2004}:
+
+\begin{displayquote}
+ Never before I have heard uglier songs than those of the Vikings in
+ Slesvig. The growling sound coming from their throats reminds me of dogs
+ howling, only more untamed.
+\end{displayquote}
%A majority of the music is not only instrumental but also contains vocal
%segments.
Berenzweig and Ellis expanded on the aforementioned research by trying to
separate singing from instrumental music\cite{berenzweig_locating_2001}.
-\todo{Incorporate this in literary framing}
-~\cite{fujihara_automatic_2006}
-~\cite{fujihara_lyricsynchronizer:_2011}
-~\cite{fujihara_three_2008}
-~\cite{mauch_integrating_2012}
-~\cite{mesaros_adaptation_2009}
-~\cite{mesaros_automatic_2008}
-~\cite{mesaros_automatic_2010}
-~%\cite{muller_multimodal_2012}
-~\cite{pedone_phoneme-level_2011}
-~\cite{yang_machine_2012}
+\todo{Incorporate this in literary framing}%
+~\cite{fujihara_automatic_2006}%
+~\cite{fujihara_lyricsynchronizer:_2011}%
+~\cite{fujihara_three_2008}%
+~\cite{mauch_integrating_2012}%
+~\cite{mesaros_adaptation_2009}%
+~\cite{mesaros_automatic_2008}%
+~\cite{mesaros_automatic_2010}%
+~%\cite{muller_multimodal_2012}%
+~\cite{pedone_phoneme-level_2011}%
+~\cite{yang_machine_2012}%
+
+
\section{Research question}
-This leads to the following research question:
+It is discutable whether the aforementioned techniques work because the
+spectral properties of a growling voice is different from the spectral
+properties of a clean singing voice. It has been found that growling voices
+have less prominent peaks in the frequency representation and are closer to
+noise then clean singing\cite{kato_acoustic_2013}. This leads us to the
+research question:
+
\begin{center}\em%
Are standard \gls{ANN} based techniques for singing voice detection
- suitable for non-standard musical genres like Death metal.
+ suitable for non-standard musical genres like \gls{dm}.
\end{center}
\chapter{Methods}
%Experiment(s) (set-up, data, results, discussion)
\section{Data \& Preprocessing}
-To run the experiments we have collected data from several \gls{dm} albums. The
-exact data used is available in Appendix~\ref{app:data}. The albums are
+To run the experiments data has been collected from several \gls{dm} albums.
+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
~\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. Every file is annotated using
-Praat~\cite{boersma_praat_2002} where the utterances are manually
-aligned to the audio. An example of an utterances are shown in
+Praat~\cite{boersma_praat_2002} where the utterances are manually aligned to
+the audio. An example of an utterances are shown in
Figures~\ref{fig:bloodstained,fig:abominations}. It is clearly visible that
within the genre of death metal there are a lot of different spectral patterns
visible.
\caption{Outline}
\end{table}
+\todo{Explain why MFCC and which parameters}
+\todo{Spectrals might be enough, no decorrelation}
+
+\section{Experiments}
+
\section{Results}
\chapter{Conclusion \& Discussion}
%Discussion section
+\todo{Novelty}
+\todo{Weaknesses}
+\todo{Dataset is not very varied but\ldots}
+
+\todo{Doom metal}
%Conclusion section
%Acknowledgements
%Statement on authors' contributions
repositories / asr1617.git / blobdiff