[mirtoolbox] Re: MIR classify
- From: Martín Hartmann <martinarielhartmann@xxxxxxxxx>
- To: mirtoolbox@xxxxxxxxxxxxx
- Date: Mon, 15 Nov 2010 19:35:43 +0200
Dear Ersin,
You can extract multiple analytic features from the train and test
sets at the same time with mirclassify. Simply create an object for
each feature (e.g., spectral centroid) or feature set (e.g., MFCCs)
you wish to use. For example: mirclassify(test, {mfcc(test),
centroid(test)}, train, {mfcc(train), centroid(train)})
I strongly recommend you to read the page 163 of the MIRtoolbox Users'
Guide: https://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/mirtoolbox/MIRtoolbox%20Users%20Guide%201.3
Best,
Martin Hartmann
On Sun, Nov 14, 2010 at 11:49 PM, Ersin Karcı <ersin.kar@xxxxxxxxx> wrote:
>
> Dear All,
> I am trying to use mirclassify, with my additional features. As far as I
> understood the there are only one set of features per one audio sample used
> as an input (mirclassify(test,mfcctest,train,mfcctrain) where mfcc test is
> 13x1 feature vector).
> In my problem I am calculating those features per frames. If we assume that
> the audio sample is 10 seconds length, with 50msec frames I obtain 13x20 mfcc
> coefficients per audio sample. While using this as the input I obtain the
> following error:
> ??? Subscripted assignment dimension mismatch.
> Error in ==> mirclassify.mirclassify>integrate at 176
> vtl(:,l) = vl;
> Error in ==> mirclassify.mirclassify at 39
> vt = integrate(vt,get(dt{i},'Data'),lvt,norml);
>
> The same issue is the same for other features.
> Can you help me with this issue?
> Regards
> Ersin KARCI
--
Martin Ariel Hartmann
Lic. in Psychology | Music, Mind and Technology MA Candidate
+358 (0) 4 6581 2384 | martinarielhartmann@xxxxxxxxx
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