changeset 63:2994e5e485e7

audio Tidy #1

--- a/draft.tex	Fri Mar 16 23:18:35 2012 +0000
+++ b/draft.tex	Sat Mar 17 00:04:51 2012 +0000
@@ -749,30 +749,19 @@
       }
     \end{fig}
 
-    \subsection{Content analysis/Sound Categorisation}
-	 Using analogous definitions of differential entropy, the methods outlined
-	 in the previous section are equally applicable to continuous random variables.
-	 In the case of music, where expressive properties such as dynamics, tempo,
-	 timing and timbre are readily quantified on a continuous scale, the information
-	 dynamic framework thus may also be considered.
+    \subsection{Audio based content analysis}
+     Using analogous definitions of differential entropy, the methods outlined
+     in the previous section are equally applicable to continuous random variables.
+     In the case of music, where expressive properties such as dynamics, tempo,
+     timing and timbre are readily quantified on a continuous scale, the information
+     dynamic framework may also be considered.
 
-	 In \cite{Dubnov2006}, Dubnov considers the class of stationary Gaussian
-	 processes. For such processes, the entropy rate may be obtained analytically
-	 from the power spectral density of the signal, allowing the multi-information
-	 rate to be subsequently obtained. 
-%	 Local stationarity is assumed, which may be achieved by windowing or 
-%	 change point detection \cite{Dubnov2008}. 
-	 %TODO
-	 mention non-gaussian processes extension Similarly, the predictive information
-	 rate may be computed using a Gaussian linear formulation CITE. In this view,
-	 the PIR is a function of the correlation  between random innovations supplied
-	 to the stochastic process.  %Dubnov, MacAdams, Reynolds (2006) %Bailes and
-	 Dean (2009)
-
-	% !!! FIXME
-		[ Continuous domain information ]
-		[Audio based music expectation modelling]
-		[ Gaussian processes]
+     In \cite{Dubnov2006}, Dubnov considers the class of stationary Gaussian
+     processes. For such processes, the entropy rate may be obtained analytically
+     from the power spectral density of the signal, allowing the multi-information
+     rate to be subsequently obtained. One aspect demanding further investigation
+     involves the comparison of alternative measures of predictability. In the case of the PIR, a Gaussian linear formulation is applicable, indicating that the PIR is a function of the correlation  between random innovations supplied to the stochastic process CITE.
+    % !!! FIXME
 
 
 \subsection{Beat Tracking}