up

diff --git a/Makefile b/Makefile
index f368d8e..bb74780 100644 (file)
--- a/Makefile
+++ b/Makefile
@@ -19,11 +19,11 @@ all: $(addsuffix .pdf,$(DOCS))
        $(LATEX) $(LATEXFLAGS) $<
        if $(GREP) -q '^\\bibdata{' $(basename $<).aux; then $(BIBTEX) $(BIBTEXFLAGS) $(basename $<); fi
        if $(GREP) -q '\@istfilename' $(basename $<).aux; then $(MAKEGLOSSARIES) $(MAKEGLOSSARIESFLAGSFLAGS) $(basename $<); fi
-       $(LATEX) $(LATEXFLAGS) $< | pee cat "$(GREP) -iFq 'Rerun'"\
-               && $(LATEX) $(LATEXFLAGS) $< | pee cat "$(GREP) -iFq 'Rerun'"\
-               && $(LATEX) $(LATEXFLAGS) $< | pee cat "$(GREP) -iFq 'Rerun'"\
-               && $(LATEX) $(LATEXFLAGS) $< | pee cat "$(GREP) -iFq 'Rerun'"\
-               || true
+       $(LATEX) $(LATEXFLAGS) $< | tee $(basename $@).mlog 
+       $(GREP) -iFq 'Rerun' $(basename $@).mlog && $(LATEX) $(LATEXFLAGS) $< | tee $(basename $@).mlog || true
+       $(GREP) -iFq 'Rerun' $(basename $@).mlog && $(LATEX) $(LATEXFLAGS) $< | tee $(basename $@).mlog || true
+       $(GREP) -iFq 'Rerun' $(basename $@).mlog && $(LATEX) $(LATEXFLAGS) $< | tee $(basename $@).mlog || true
+       $(RM) $(basename $@).mlog
 
 clean: $(addprefix clean-,$(DOCS))
 

diff --git a/asr.tex b/asr.tex
index 40c6e52..f149b1d 100644 (file)
--- a/asr.tex
+++ b/asr.tex
@@ -1,6 +1,6 @@
 %&asr
 \usepackage[nonumberlist,acronyms]{glossaries}
-\makeglossaries%
+%\makeglossaries%
 \newacronym{ANN}{ANN}{Artificial Neural Network}
 \newacronym{HMM}{HMM}{Hidden Markov Model}
 \newacronym{GMM}{GMM}{Gaussian Mixture Models}
@@ -59,7 +59,7 @@ physical sale and the remaining $16\%$ is made through performance and
 synchronisation revenieus. The overtake of digital formats on physical formats
 took place somewhere in 2015. Moreover, ever since twenty years the music
 industry has seen significant growth 
-again~\footnote{\url{http://www.ifpi.org/facts-and-stats.php}}.
+again\footnote{\url{http://www.ifpi.org/facts-and-stats.php}}.
 
 There has always been an interest in lyrics to music alignment to be used in
 for example karaoke. As early as in the late 1980s karaoke machines were
@@ -68,7 +68,7 @@ available, a alignment is not and it involves manual labour to create such an
 alignment.
 
 A lot of this musical distribution goes via non-official channels such as
-YouTube~\footnote{\url{https://youtube.com}} in which fans of the performers
+YouTube\footnote{\url{https://youtube.com}} in which fans of the performers
 often accompany the music with synchronized lyrics. This means that there is an
 enormous treasure of lyrics-annotated music available but not within our reach
 since the subtitles are almost always hardcoded into the video stream and thus
@@ -83,9 +83,9 @@ or growling. Growling is heavily used in extreme metal genres such as \gls{dm}
 but 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
+by Louis Armstrong\cite{sakakibara_growl_2004}. It might even be traced back
 to viking times. For example, an arab merchant visiting a village in Denmark
-wrote in the tenth century~\cite{friis_vikings_2004}:
+wrote in the tenth century\cite{friis_vikings_2004}:
 
 \begin{displayquote}
        Never before I have heard uglier songs than those of the Vikings in
@@ -98,7 +98,7 @@ wrote in the tenth century~\cite{friis_vikings_2004}:
 %Literature overview / related work
 \section{Related work}
 The field of applying standard speech processing techniques on music started in
-the late 90s~\cite{saunders_real-time_1996,scheirer_construction_1997} and it
+the late 90s\cite{saunders_real-time_1996,scheirer_construction_1997} and it
 was found that music has different discriminating features compared to normal
 speech.
 
@@ -140,18 +140,18 @@ research question:
 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/}}.
+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.
+\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
-Praat~\cite{boersma_praat_2002} where the utterances are manually aligned to
+Praat\cite{boersma_praat_2002} where the utterances are manually aligned to
 the audio. Examples of utterances are shown in
-Figures~\ref{fig:bloodstained,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
+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.
 
 \begin{figure}[ht]
@@ -211,6 +211,9 @@ similar on Death Metal
        \caption{Outline}
 \end{table}
 
+\section{Features}
+
+
 \todo{Explain why MFCC and which parameters}
 \todo{Spectrals might be enough, no decorrelation}