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
=================================================================
Instructions for joining and leaving this list, remarks about the
problem of INAPPROPRIATE MESSAGES, and archives are available at
http://jse.stat.ncsu.edu/
=================================================================
