processed some of the comments

[asr1617.git] / methods.tex

diff --git a/methods.tex b/methods.tex
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--- a/methods.tex
+++ b/methods.tex
@@ -5,14 +5,13 @@
 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/}}.
-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
-waveform, $1-8000$Hz spectrals and annotations are shown. It is clearly visible
-that within the genre of death metal there are a different spectral patterns
-visible.
+correct samplerate utilizing \emph{SoX}%
+\footnote{\url{http://sox.sourceforge.net/}}.  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 waveform, $1-8000$Hz spectrals and
+annotations are shown. It is clearly visible that within the genre of death
+metal there are different spectral patterns visible over time.
 
 \begin{figure}[ht]
        \centering
@@ -28,12 +27,11 @@ visible.
                \emph{Enthroned Abominations}}\label{fig:abominations}
 \end{figure}
 
-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 vocals produced by \emph{Cannibal Corpse} are bordering
-regular shouting. 
+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
+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
@@ -80,8 +78,8 @@ performance\cite{you_comparative_2015}. The actual conversion is done using the
 \emph{python\_speech\_features}%
 \footnote{\url{https://github.com/jameslyons/python_speech_features}} package.
 
-\gls{MFCC} features are nature inspired and built incrementally in several
-steps.
+\gls{MFCC} features are inspired by human auditory processing inspired and
+built incrementally in several steps.
 \begin{enumerate}
        \item The first step in the process is converting the time representation
                of the signal to a spectral representation using a sliding window with
@@ -92,13 +90,21 @@ steps.
                impossible so it is arguable that the window size is very small.
        \item The standard \gls{FT} gives a spectral representation that has
                linearly scaled frequencies. This scale is converted to the \gls{MS}
-               using triangular overlapping windows.
-       \item The log is taken of the Mel frequencies. This step is inspired by the
-               \emph{Weber-Fechner} law that describes how humans perceive physical
+               using triangular overlapping windows to get a more tonotopic
+               representation trying to match the actual representation in the cochlea
+               of the human ear.
+       \item The \emph{Weber-Fechner} law that describes how humans perceive physical
                magnitudes\footnote{Fechner, Gustav Theodor (1860). Elemente der
-               Psychophysik}
-       \item To decorrelate the signal a \gls{DCT} is applied. The \gls{MFCC}
-               features are then the amplitudes of the spectrum.
+               Psychophysik} and it was found that energy is perceived in logarithmic
+               increments. This means that twice the amount of decibels does not mean
+               twice the amount of perceived loudness. Therefore in this step log is
+               taken of energy or amplitude of the \gls{MS} frequency spectrum to
+               closer match the human hearing.
+       \item The amplitudes of the spectrum are highly correlated and therefore
+               the last step is a decorrelation step. \Gls{DCT} is applied on the
+               amplitudes interpreted as a signal. \Gls{DCT} is a technique of
+               describing a signal as a combination of several primitive cosine
+               functions.
 \end{enumerate}
 
 \section{\gls{ANN} Classifier}