Corection typo: Should read 'Whiten intra class scatter'
"Mark Harrison" <[EMAIL PROTECTED]> wrote in message
news:FIif8.16518$[EMAIL PROTECTED].;
> Good places to start:
>
> Optimal feature extractors, that's better than PCA because you whiten your
> inter class scatter and so put all inter class comparisons on the same
> level. The good thing is this will also reduce your feature vector
> dimensionality to c-1 (where c is # classes). PCA will not do this.
>
> Check the stats of each class, is it Gaussian or known pdf? Apply
> parameteric classifier if so.
>
> However you are lucky if you get good classification after this, so you
will
> probably need non linear, non parametric classifiers. Try K nearest
> neighobour, but that might take the age of the Universe so use a
condensing
> algorithm first to get a smaller representative set.
>
> Matlab is what I use for coding, there are a lot of free toolboxes around.
> Mostly I write my own though.
>
> Best wishes
>
> Andrew
>
>
> "Rishabh Gupta" <[EMAIL PROTECTED]> wrote in message
> news:a4eje9$ip8$[EMAIL PROTECTED].;
> > Hi All,
> > I'm a research student at the Department Of Electronics, University
Of
> > York, UK. I'm working a project related to music analysis and
> > classification. I am at the stage where I perform some analysis on music
> > files (currently only in MIDI format) and extract about 500 variables
that
> > are related to music properties like pitch, rhythm, polyphony and
volume.
> I
> > am performing basic analysis like mean and standard deviation but then I
> > also perform more elaborate analysis like measuring complexity of melody
> and
> > rhythm.
> >
> > The aim is that the variables obtained can be used to perform a number
of
> > different operations.
> > - The variables can be used to classify / categorise each piece of
> > music, on its own, in terms of some meta classifier (e.g. rock, pop,
> > classical).
> > - The variables can be used to perform comparison between two files.
A
> > variable from one music file can be compared to the equivalent variable
in
> > the other music file. By comparing all the variables in one file with
the
> > equivalent variable in the other file, an overall similarity measurement
> can
> > be obtained.
> >
> > The next stage is to test the ability of the of the variables obtained
to
> > perform the classification / comparison. I need to identify variables
that
> > are redundant (redundant in the sense of 'they do not provide any
> > information' and 'they provide the same information as the other
> variable')
> > so that they can be removed and I need to identify variables that are
> > distinguishing (provide the most amount of information).
> >
> > My Basic Questions Are:
> > - What are the best statistical techniques / methods that should be
> > applied here. E.g. I have looked at Principal Component Analysis; this
> would
> > be a good method to remove the redundant variables and hence reduce some
> the
> > amount of data that needs to be processed. Can anyone suggest any other
> > sensible statistical anaysis methods?
> > - What are the ideal tools / software to perform the clustering /
> > classification. I have access to SPSS software but I have never used it
> > before and am not really sure how to apply it or whether it is any good
> when
> > dealing with 100s of variables.
> >
> > So far I have been analysing each variable on its own 'by eye' by
plotting
> > the mean and sd for all music files. However this approach is not
feasible
> > in the long term since I am dealing with such a large number of
variables.
> > In addition, by looking at each variable on its own, I do not find
> clusters
> > / patterns that are only visible through multivariate analysis. If
anyone
> > can recommend a better approach I would be greatly appreciated.
> >
> > Any help or suggestion that can be offered will be greatly appreciated.
> >
> > Many Thanks!
> >
> > Rishabh Gupta
> >
> >
>
>
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