%&asr
+\usepackage[nonumberlist,acronyms]{glossaries}
+\makeglossaries%
+\newacronym{ANN}{ANN}{Artificial Neural Network}
+\newacronym{HMM}{HMM}{Hidden Markov Model}
+\newacronym{GMM}{GMM}{Gaussian Mixture Models}
+\newacronym{DHMM}{DHMM}{Duration-explicit \acrlong{HMM}}
+\newacronym{HTK}{HTK}{\acrlong{HMM} Toolkit}
+\newacronym{FA}{FA}{Forced alignment}
+\newacronym{MFC}{MFC}{Mel-frequency cepstrum}
+\newacronym{MFCC}{MFCC}{\acrlong{MFC} coefficient}
+\newglossaryentry{dm}{name={Death Metal},
+ description={is an extreme heavy metal music style with growling vocals and
+ pounding drums}}
+
\begin{document}
-%Titlepage
+\frontmatter{}
+
\maketitleru[
course={(Automatic) Speech Recognition},
institute={Radboud University Nijmegen},
- authorstext={Author:}]
+ authorstext={Author:},
+ pagenr=1]
\listoftodos[Todo]
-\nocite{*}
+\tableofcontents
+
+%Glossaries
+\glsaddall{}
+\printglossaries%
+
+\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}
+
+
+%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)}
+
+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
+
+%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
+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
+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
+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.
+
+\begin{figure}[ht]
+ \centering
+ \includegraphics[width=.7\linewidth]{cement}
+ \caption{A vocal segment of the \emph{Cannibal Corpse} song
+ \emph{Bloodstained Cement}}\label{fig:bloodstained}
+\end{figure}
+
+\begin{figure}[ht]
+ \centering
+ \includegraphics[width=.7\linewidth]{abominations}
+ \caption{A vocal segment of the \emph{Disgorge} song
+ \emph{Enthroned Abominations}}\label{fig:abominations}
+\end{figure}
+
+The data is collected from two\todo{more in the future}\ 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 second band is called \emph{Disgorge} and make even more
+violent music. The growls of the lead singer sound more like a coffee grinder
+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.
+
+\chapter{Conclusion \& Discussion}
+%Discussion section
+%Conclusion section
+%Acknowledgements
+%Statement on authors' contributions
+%(Appendices)
+\appendix
+\chapter{Experimental data}\label{app:data}
+\begin{table}[h]
+ \centering
+ \begin{tabular}{cllll}
+ \toprule
+ Num. & Artist & Album & Song & Duration\\
+ \midrule
+ 00 & Cannibal Corpse & A Skeletal Domain & High Velocity Impact Spatter & 04:06.91\\
+ 01 & Cannibal Corpse & A Skeletal Domain & Sadistic Embodiment & 03:17.31\\
+ 02 & Cannibal Corpse & A Skeletal Domain & Kill or Become & 03:50.67\\
+ 03 & Cannibal Corpse & A Skeletal Domain & A Skeletal Domain & 03:38.77\\
+ 04 & Cannibal Corpse & A Skeletal Domain & Headlong Into Carnage & 03:01.25\\
+ 05 & Cannibal Corpse & A Skeletal Domain & The Murderer's Pact & 05:05.23\\
+ 06 & Cannibal Corpse & A Skeletal Domain & Funeral Cremation & 03:41.89\\
+ 07 & Cannibal Corpse & A Skeletal Domain & Icepick Lobotomy & 03:16.24\\
+ 08 & Cannibal Corpse & A Skeletal Domain & Vector of Cruelty & 03:25.15\\
+ 09 & Cannibal Corpse & A Skeletal Domain & Bloodstained Cement & 03:41.99\\
+ 10 & Cannibal Corpse & A Skeletal Domain & Asphyxiate to Resuscitate & 03:47.40\\
+ 11 & Cannibal Corpse & A Skeletal Domain & Hollowed Bodies & 03:05.80\\
+ 12 & Disgorge & Parallels of Infinite Torture & Revealed in Obscurity & 05:13.20\\
+ 13 & Disgorge & Parallels of Infinite Torture & Enthroned Abominations & 04:05.39\\
+ 14 & Disgorge & Parallels of Infinite Torture & Atonement & 02:57.36\\
+ 15 & Disgorge & Parallels of Infinite Torture & Abhorrent Desecration of Thee Iniquity & 04:17.20\\
+ 16 & Disgorge & Parallels of Infinite Torture & Forgotten Scriptures & 02:01.72\\
+ 17 & Disgorge & Parallels of Infinite Torture & Descending Upon Convulsive Devourment & 04:38.85\\
+ 18 & Disgorge & Parallels of Infinite Torture & Condemned to Sufferance & 04:57.59\\
+ 19 & Disgorge & Parallels of Infinite Torture & Parallels of Infinite Torture & 05:03.33\\
+ 20 & Disgorge & Parallels of Infinite Torture & Asphyxiation of Thee Oppressed & 05:42.37\\
+ 21 & Disgorge & Parallels of Infinite Torture & Ominous Sigils of Ungodly Ruin & 04:59.15\\
+ \bottomrule
+ \end{tabular}
+ \caption{Songs used in the experiments}
+\end{table}
+
\bibliographystyle{ieeetr}
\bibliography{asr}
\end{document}
repositories / asr1617.git / blobdiff