brush up introduction

[asr1617.git] / asr.tex

diff --git a/asr.tex b/asr.tex
index 4899bd1..1316967 100644 (file)
--- a/asr.tex
+++ b/asr.tex
@@ -64,6 +64,21 @@ along. Because of this interest it is very useful to device automatic
 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.
@@ -103,11 +118,19 @@ separate singing from instrumental music\cite{berenzweig_locating_2001}.
 ~\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}
@@ -115,8 +138,8 @@ This leads to the following research question:
 
 %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
@@ -124,8 +147,8 @@ 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. 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.