-DOCS:=asr
+DOCS:=asr proposal
GREP?=grep
LATEX?=pdflatex
BIBTEX?=bibtex
t\cite{pedone_phoneme-level_2011}
t\cite{yang_machine_2012}
+
+%Introduction, leading to a clearly defined research question
+%Literature overview / related work
+%Methodology
+%Experiment(s) (set-up, data, results, discussion)
+%Discussion section
+%Conclusion section
+%Acknowledgements
+%Statement on authors' contributions
+%(Appendices)
+
\bibliographystyle{ieeetr}
\bibliography{asr}
\end{document}
--- /dev/null
+\documentclass[a4paper]{article}
+
+\usepackage[british]{babel}
+
+\usepackage{geometry} % Papersize
+\usepackage{hyperref} % Hyperlinks
+
+\urlstyle{same}
+\hypersetup{%
+ pdftitle={},
+ pdfauthor={Mart Lubbers},
+ pdfsubject={},
+ pdfcreator={Mart Lubbers},
+ pdfproducer={Mart Lubbers},
+ pdfkeywords={},
+ hidelinks=true
+}
+
+\title{(Automatic) Speech Recognition\\{\large Proposal}}
+\author{Mart Lubbers\\
+ {\small\href{mailto:mart@martlubbers.net}{mart@martlubbers.net}}}
+\date{\today}
--- /dev/null
+%&proposal
+\begin{document}
+\maketitle
+
+My proposed research consists of two questions of which the thesis will answer
+at least one.
+
+The first topic is singing voice detection. Singing voice detection has been
+done on numerous amounts of musical styles ranging from unconventional styles
+like Beijing opera to conventional pop music. Moreover, the problem has been
+tackled using myriads of different approaches such as HMMs with different
+acoustic model types but also machine learned feature sets. I would like to
+explore how HMM based techniques perform on extreme heavy metal styles to see
+how well it can detect growling and how classifier might be adapted to perform
+better.
+
+Singing voice detection is often used as a preprocessing step for song lyrics
+forced alignment. If the time permits I would like to explore forced alignment
+using existing phone models on extreme heavy metal styles. Features probably
+need to be changed to improve performance since growling is very different from
+regular singing and speaking.
+
+The data for this will be coming from my personal collection audio CDs.
+
+\end{document}