update for tomorrow

diff --git a/asr.bib b/asr.bib
index 570488f..594ead2 100644 (file)
--- a/asr.bib
+++ b/asr.bib
@@ -211,4 +211,25 @@
        journal = {Glot international},
        author = {Boersma, Paulus Petrus Gerardus and {others}},
        year = {2002}
        journal = {Glot international},
        author = {Boersma, Paulus Petrus Gerardus and {others}},
        year = {2002}

+}
+
+@inproceedings{saunders_real-time_1996,
+       title = {Real-time discrimination of broadcast speech/music},
+       volume = {2},
+       booktitle = {Acoustics, {Speech}, and {Signal} {Processing}, 1996. {ICASSP}-96. {Conference} {Proceedings}., 1996 {IEEE} {International} {Conference} on},
+       publisher = {IEEE},
+       author = {Saunders, John},
+       year = {1996},
+       pages = {993--996},
+       file = {REAL-TIME DISCRIMINATION OF BROADCAST SPEECH/MUSIC - Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings., 1996 IEEE Inte - saunders_j2.pdf:/home/mrl/.mozilla/firefox/7b4r727h.default-1470981082057/zotero/storage/H9PG94BN/saunders_j2.pdf:application/pdf}
+}
+
+@inproceedings{scheirer_construction_1997,
+       title = {Construction and evaluation of a robust multifeature speech/music discriminator},
+       volume = {2},
+       booktitle = {Acoustics, {Speech}, and {Signal} {Processing}, 1997. {ICASSP}-97., 1997 {IEEE} {International} {Conference} on},
+       publisher = {IEEE},
+       author = {Scheirer, Eric and Slaney, Malcolm},
+       year = {1997},
+       pages = {1331--1334}

 }
\ No newline at end of file
 }
\ No newline at end of file

diff --git a/asr.tex b/asr.tex
index e7c6297..4899bd1 100644 (file)
--- a/asr.tex
+++ b/asr.tex
@@ -9,6 +9,7 @@
 \newacronym{FA}{FA}{Forced alignment}
 \newacronym{MFC}{MFC}{Mel-frequency cepstrum}
 \newacronym{MFCC}{MFCC}{\acrlong{MFC} coefficient}
 \newacronym{FA}{FA}{Forced alignment}
 \newacronym{MFC}{MFC}{Mel-frequency cepstrum}
 \newacronym{MFCC}{MFCC}{\acrlong{MFC} coefficient}

+\newacronym{IFPI}{IFPI}{International Federation of the Phonographic Industry}

 \newglossaryentry{dm}{name={Death Metal},
        description={is an extreme heavy metal music style with growling vocals and
        pounding drums}}
 \newglossaryentry{dm}{name={Death Metal},
        description={is an extreme heavy metal music style with growling vocals and
        pounding drums}}


@@ -26,62 +27,89 @@
 \tableofcontents
 
 %Glossaries
 \tableofcontents
 
 %Glossaries

-\glsaddall{}
-\printglossaries%
+%\glsaddall{}
+%\printglossaries

 
 \mainmatter{}
 
 \mainmatter{}

-Berenzweig and Ellis use acoustic classifiers from speech recognition as a
-detector for singing lines.  They achive 80\% accuracy for forty 15 second
-exerpts. They mention people that wrote signal features that discriminate
-between speech and music. Neural net
-\glspl{HMM}~\cite{berenzweig_locating_2001}.
-
-In 2014 Dzhambazov et al.\ applied state of the art segmentation methods to
-polyphonic turkish music, this might be interesting to use for heavy metal.
-They mention Fujihara (2011) to have a similar \gls{FA} system. This method uses
-phone level segmentation, first 12 \gls{MFCC}s. They first do vocal/non-vocal
-detection, then melody extraction, then alignment. They compare results with
-Mesaros \& Virtanen, 2008~\cite{dzhambazov_automatic_2014}. Later they
-specialize in long syllables in a capella. They use \glspl{DHMM} with
-\glspl{GMM} and show that adding knowledge increases alignment (bejing opera
-has long syllables)~\cite{dzhambazov_automatic_2016}.
-
-t\cite{fujihara_automatic_2006}
-t\cite{fujihara_lyricsynchronizer:_2011}
-t\cite{fujihara_three_2008}
-t\cite{mauch_integrating_2012}
-t\cite{mesaros_adaptation_2009}
-t\cite{mesaros_automatic_2008}
-t\cite{mesaros_automatic_2010}
-t\cite{muller_multimodal_2012}
-t\cite{pedone_phoneme-level_2011}
-t\cite{yang_machine_2012}
+%Berenzweig and Ellis use acoustic classifiers from speech recognition as a
+%detector for singing lines.  They achive 80\% accuracy for forty 15 second
+%exerpts. They mention people that wrote signal features that discriminate
+%between speech and music. Neural net
+%\glspl{HMM}~\cite{berenzweig_locating_2001}.
+%
+%In 2014 Dzhambazov et al.\ applied state of the art segmentation methods to
+%polyphonic turkish music, this might be interesting to use for heavy metal.
+%They mention Fujihara (2011) to have a similar \gls{FA} system. This method uses
+%phone level segmentation, first 12 \gls{MFCC}s. They first do vocal/non-vocal
+%detection, then melody extraction, then alignment. They compare results with
+%Mesaros \& Virtanen, 2008~\cite{dzhambazov_automatic_2014}. Later they
+%specialize in long syllables in a capella. They use \glspl{DHMM} with
+%\glspl{GMM} and show that adding knowledge increases alignment (bejing opera
+%has long syllables)~\cite{dzhambazov_automatic_2016}.
+%

 
 
 %Introduction, leading to a clearly defined research question
 \chapter{Introduction}
 \section{Introduction}
 
 
 %Introduction, leading to a clearly defined research question
 \chapter{Introduction}
 \section{Introduction}

-Music is a leading type of data distributed on the internet. Regular music
-distribution is almost entirely digital and services like Spotify and YouTube
-allow one to listen to almost any song within a few clicks. Moreover, there are
-myriads of websites offering lyrics of songs.
-
-\todo{explain relevancy, (preprocessing for lyric alignment)}
+The \gls{IFPI} stated that about $43\%$ of music revenue rises from digital
+distribution. The overtake on physical formats took place somewhere in 2015 and
+since twenty years the music industry has seen significant
+growth~\footnote{\url{http://www.ifpi.org/facts-and-stats.php}}.
+
+A lot of this musical distribution goes via non-official channels such as
+YouTube~\footnote{\url{https://youtube.com}} in which fans of the musical group
+accompany the music with synchronized lyrics so that users can sing or read
+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.
+
+
+%A majority of the music is not only instrumental but also contains vocal
+%segments.
+%
+%Music is a leading type of data distributed on the internet. Regular music
+%distribution is almost entirely digital and services like Spotify and YouTube
+%allow one to listen to almost any song within a few clicks. Moreover, there are
+%myriads of websites offering lyrics of songs.
+%
+%\todo{explain relevancy, (preprocessing for lyric alignment)}
+%
+%This leads to the following research question:
+%\begin{center}\em%
+%      Are standard \gls{ANN} based techniques for singing voice detection
+%      suitable for non-standard musical genres like Death metal.
+%\end{center}

 
 

+%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
+was found that music has different discriminating features compared to normal
+speech.
+
+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}
+
+\section{Research question}

 This leads to the following research question:
 \begin{center}\em%
        Are standard \gls{ANN} based techniques for singing voice detection
        suitable for non-standard musical genres like Death metal.
 \end{center}
 
 This leads to the following research question:
 \begin{center}\em%
        Are standard \gls{ANN} based techniques for singing voice detection
        suitable for non-standard musical genres like Death metal.
 \end{center}
 

-%Literature overview / related work
-\section{Related work}
-
-Singing/non-singing detection has been fairecent topic of interest in the
-academia. Just in 2001 Berenzweig and Ellis~\cite{berenzweig_locating_2001}
-researched singing voice detection in stead of the more founded topic of
-discerning music from regular speech. In their research 
-

 \chapter{Methods}
 %Methodology
 
 \chapter{Methods}
 %Methodology
 


@@ -126,6 +154,42 @@ and are more shallow. The lyrics are completely incomprehensible and therefore
 some parts are not annotated with lyrics because it was too difficult to hear
 what was being sung.
 
 some parts are not annotated with lyrics because it was too difficult to hear
 what was being sung.
 

+\section{Methods}
+\todo{To remove in final thesis}
+The initial planning is still up to date. About one and a half album has been
+annotated and a framework for setting up experiments has been created.
+Moreover, the first exploratory experiments are already been executed and
+promising. In April the experimental dataset will be expanded and I will try to
+mimic some of the experiments done in the literature to see whether it performs
+similar on Death Metal
+\begin{table}[ht]
+       \centering
+       \begin{tabular}{cll}
+               \toprule
+               Month & Description\\
+               \midrule
+               March
+                       & Preparing the data\\
+                       & Preparing an experiment platform\\
+                       & Literature research\\
+               April
+                       & Running the experiments\\
+                       & Fiddle with parameters\\
+                       & Explore the possibilities for forced alignment\\
+               May
+                       & Write up the thesis\\
+                       & Possibly do forced alignment\\
+               June
+                       & Finish up thesis\\
+                       & Wrap up\\
+               \bottomrule
+       \end{tabular}
+       \caption{Outline}
+\end{table}
+
+\section{Results}
+
+

 \chapter{Conclusion \& Discussion}
 %Discussion section
 %Conclusion section
 \chapter{Conclusion \& Discussion}
 %Discussion section
 %Conclusion section