diff draft.tex @ 16:d5f63ea0f266

fixed abstract tag, tidy doc structure

--- a/draft.tex	Tue Mar 06 12:30:23 2012 +0000
+++ b/draft.tex	Tue Mar 06 15:21:35 2012 +0000
@@ -17,15 +17,16 @@
 \title{Cognitive Music Modelling: an Information Dynamics Approach}
 
 \author{
-	\IEEEauthorblockN{Samer A. Abdallah, Henrik Ekeus,}
-	\IEEEauthorblockN{Peter Foster and Mark D. Plumbley}
+	\IEEEauthorblockN{Samer A. Abdallah, Henrik Ekeus, Peter Foster}
+	\IEEEauthorblockN{Andrew Robertson and Mark D. Plumbley}
 	\IEEEauthorblockA{Centre for Digital Music\\
 		Queen Mary University of London\\
-		Mile End Road, London E1 4NS}}
+		Mile End Road, London E1 4NS\\
+		Email:}}
 
 \maketitle
-\abstract{People take in information when perceiving music.  With it they continually build predictive models of what is going to happen.  There is a relationship between information measures and how we perceive music.  An information theoretic approach to music cognition is thus a fruitful avenue of research.
-}
+\begin{abstract}People take in information when perceiving music.  With it they continually build predictive models of what is going to happen.  There is a relationship between information measures and how we perceive music.  An information theoretic approach to music cognition is thus a fruitful avenue of research.
+\end{abstract}
 
 
 \section{Expectation and surprise in music}
@@ -210,31 +211,22 @@
 	and selective or evaluative phases \cite{Boden1990}, and would have
 	applications in tools for computer aided composition.
 
-\section{Information Dynamics Approach}
 
-\subsection{Re-iterate core hypothesis}
+\section{Information Dynamics in Analysis}
 
-\subsection{models/parameters/observations}
-The grouping of elements into past, present and future..s
-\subsection{Information measures}
-Predictive information rate as a measure of structure 
-Cruchfield papers, anatomy of abit
-\subsection{Case of this approach being good at modelling music cognition}
-Inverted U
-\section{Applications}
-\subsection{In Analysis}
+ 	\subsection{Musicological Analysis}
 	refer to the work with the analysis of minimalist pieces
 	
-	Content analysis - Sound Categorisation.  Using Information Dynamics it is possible to segment music.  From there we can then use this to search large data sets. Determine musical structure for the purpose of playlist navigation and search. (Peter)
+	\subsection{Content analysis/Sound Categorisation}.  Using Information Dynamics it is possible to segment music.  From there we can then use this to search large data sets. Determine musical structure for the purpose of playlist navigation and search. 
+	\emph{Peter}
 
 \subsection{Beat Tracking}
-  Bayesian belief can be used to predict when things happen (as oppose to just what happens).  Information Dynamics of?
-  
+ \emph{Andrew}  
 
 
 \section{Information Dynamics as Design Tool}
 
-In addition to applying Information Dynamics to analysis, it is also possible use this approach in design, such as the composition of musical materials.
+In addition to applying information dynamics to analysis, it is also possible use this approach in design, such as the composition of musical materials.
 By providing a framework for linking information theoretic measures to the control of generative processes, it becomes possible to steer the output of these processes to match a criteria defined by these measures. 
 For instance outputs of a stochastic musical process could be filtered to match constraints defined by a set of information theoretic measures.  
 
@@ -256,7 +248,7 @@
 	
 When the Melody Triangle is used, regardless of whether it is as a screen based system, or as an interactive installation, it involves a mapping to this statistical space. 
 When the user, through the interface, selects a position within the triangle, the corresponding transition matrix is returned. 
-Figure x shows how the triangle maps to different measures of redundancy, entropy rate and predictive information rate.\emph{self-plagiarised}
+Figure x shows how the triangle maps to different measures of redundancy, entropy rate and predictive information rate.\emph{self-plagiarised}
 	
 Each corner corresponds to three different extremes of predictability and unpredictability, which could be loosely characterised as ÔperiodicityÕ, ÔnoiseÕ and ÔrepetitionÕ. 
 Melodies from the ÔnoiseÕ corner have no discernible pattern; they have high entropy rate, low predictive information rate and low redundancy. 
@@ -289,7 +281,8 @@
 We carried out a preliminary study that sought to identify any correlation between aesthetic preference and the information theoretical measures of the Melody Triangle. 
 In this study participants were asked to use the screen based interface but it was simplified so that all they could do was move tokens around. 
 To help discount visual biases, the axes of the triangle would be randomly rearranged for each participant.\emph{self-plagiarised}
-
The study was divided in to two parts, the first investigated musical preference with respect to single melodies at different tempos. 
+
+The study was divided in to two parts, the first investigated musical preference with respect to single melodies at different tempos. 
 In the second part of the study, a back- ground melody is playing and the participants are asked to find a second melody that Õworks wellÕ with the background melody. 
 For each participant this was done four times, each with a different background melody from four different areas of the Melody Triangle. 
 For all parts of the study the participants were asked to ÔmarkÕ, by pressing the space bar, whenever they liked what they were hearing.\emph{self-plagiarised}   
@@ -319,5 +312,5 @@
 \section{Conclusion}
 
 \bibliographystyle{unsrt}
-{\bibliography{all,c4dm}}
+{\bibliography{all,c4dm,nime}}
 \end{document}