\section{Introduction}
The primary medium for music distribution is rapidly changing from physical
media to digital media. In 2016 the \gls{IFPI} stated that about $50\%$ of
music revenue arises from digital distribution. Another $34\%$ arises from the
physical sale and the remaining $16\%$ is made through performance and
synchronisation revenues. 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}}.

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 became
available for consumers. Lyrics for tracks are in almost all cases amply
available. However, a temporal alignment of the lyrics is not and creating it
involves manual labour.

A lot of the current day music distribution goes via non-official channels
such as 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. However, the
data is not within our reach since the subtitles are almost always hardcoded
into the video stream and thus not directly accessible as data. It sparks the
ideas for creating automatic techniques for segmenting instrumental and vocal
parts of a song, apply forced temporal alignment or possible even apply lyrics
recognition audio data.

These techniques are heavily researched and working systems have been created
for segmenting audio and even forced temporal alignment (e.g.\ 
LyricSynchronizer~\cite{fujihara_lyricsynchronizer:_2011}). However, these
techniques are designed to detect a clean singing voice and have not been
tested on so-called \emph{extended vocal techniques} such as grunting 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 to
viking times. For example, an arab merchant visiting a village in Denmark 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}

%Literature overview / related work
\section{Related work}
Applying speech related processing and classification techniques on music
already started in the late 90s. Saunders et al.\ devised a technique to
classify audio in the categories \emph{Music} and \emph{Speech}. They found
that music has different properties than speech. Music uses a wider spectral
bandwidth in which events happen. Music contains more tonality and rhythm.
Multivariate Gaussian classifiers were used to discriminate the categories with
an average accuracy of $90\%$~\cite{saunders_real-time_1996}.

Williams and Ellis were inspired by the aforementioned research and tried to
separate the singing segments from the instrumental segments~%
\cite{williams_speech/music_1999}. Their results were later verified by
Berenzweig and Ellis~\cite{berenzweig_locating_2001}. The latter became the de
facto literature on singing voice detection. Both show that features derived
from \gls{PPF} such as energy are highly effective in separating speech from
non-speech signals such as music. The data used in the experiments was
segmented in to segments that only contained data from one class. The
classifier determined the classper sample.

Later, Berenzweig showed singing voice segments to be more useful for artist
classification and used an \gls{ANN} (\gls{MLP}) using \gls{PLP} coefficients
to detect a singing voice~\cite{berenzweig_using_2002}. Nwe et al.\ showed that
there is not much difference in accuracy when using different features founded
in speech processing. They tested several features and found accuracies differ
less than a few percent. Moreover, they found that others have tried to tackle
the problem using myriads of different approaches such as using \gls{ZCR},
\gls{MFCC} and \gls{LPCC} as features and \glspl{HMM} or \glspl{GMM} as
classifiers~\cite{nwe_singing_2004}.

Fujihara et al.\ took the idea to a next level by attempting to do \gls{FA} on
music. Their approach is a three step approach. The first step is reducing the
accompaniment levels, secondly the vocal segments are separated from the
non-vocal segments using a simple two-state \gls{HMM}. The chain is concluded
by applying \gls{Viterbi} alignment on the segregated signals with the lyrics.
The system showed accuracy levels of $90\%$ on Japanese music~%
\cite{fujihara_automatic_2006}. Later they improved hereupon~%
\cite{fujihara_three_2008} and even made a ready to use karaoke application
that can do the temporal lyrics alignment online~%
\cite{fujihara_lyricsynchronizer:_2011}.

Singing voice detection can also be seen as a binary genre recognition problem.
Therefore the techniques used in that field might be of use.  Genre recognition
has a long history that can be found in the survey by
Sturm~\cite{sturm_survey_2012}. It must be noted that of all the $485$ papers
cited by Sturm only one master thesis is applying genre recognition on heavy
metal genres~\cite{tsatsishvili_automatic_2011}.

Singing voice detection has been tried on less conventional styles in the past.
Dzhambazov et al.\ proposed to align long syllables in Beijing Opera to the
audio~\cite{dzhambazov_automatic_2016}. Beijing Opera sometimes contains
growling like vocals. Dzhambazov also tried aligning lyrics to audio in
classical Turkish music~\cite{dzhambazov_automatic_2014}.

\section{Research question}
It is debatable 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-like
vocals have less prominent peaks in the frequency representation and are closer
to noise than clean singing~\cite{kato_acoustic_2013}. This leads us to the
research question:

\begin{center}\em%
	Are standard techniques for singing voice detection suitable for
	non-standard musical genres containing extreme vocal styles?
\end{center}
