"Rishabh Gupta" <[EMAIL PROTECTED]> wrote in message
a4eje9$ip8$[EMAIL PROTECTED]">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.
A useful exposition of techniques for initial investigation of
multivariate data set is given at
http://www.sas.com/service/library/periodicals/obs/obswww22/
If you point your browser at " Andrews plots " you will find more.
My inclination would be to start with an Andrews plot, possibly
using principal component scores for about 20 music files from several
genres. This will enable you to find linear combinations of variable which
best separate the genres. The technique and examples is set out in:
Gnanadesikan:Multivariate Data Analysis, but this is an old
reference.
I hope this helps Jim Snow
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