libsvm

1. Collect Training Data: (with your own codes to create TrainingData)
   

fprintf(fp, "3 "); // label: 1, 2 ...
for (j = 0; j < 12; j++)
fprintf(fp, "%d:%f ", j, mel_cep[j]);
fprintf(fp, "\n");

2. Scale training data:
   

svm-scale.exe -l -1 -u 1 -s range1 TrainingData > TrainingData.scale 

3. Get parameters and Train data with parameters
   

python grid.py TrainingData.scale

Got the best parameters c, g (2.0, 2.0)

svm-train.exe -c 2 -g 2 TrainingData.scale

The output is TrainingData.scale.model
4. Test/Run classificaiton: same way to get test data:
   

//fprintf(fp, "1 "); // if you know the label, can get the accuracy output with this
for (j = 0; j < 12; j++)
fprintf(fp, "%d:%f ", j, mel_cep[j]);
fprintf(fp, "\n");

Scale the test data with the same scaling factor with training data:

svm-scale.exe -r range1 test_libsvm.t > test_libsvm.t.scale

Run the classification/Prediction: (write your own codes here)

svm-predict.exe test_libsvm.t.scale TrainingData.scale.model test_libsvm.t.predict

===================
To use the cross validation to check the accuracy:

svm-train.exe -v 5 TrainingData
svm-train.exe -v 5 TrainingData.scale
svm-train.exe -c 2 -g 2 -v 5 TrainingData.scale

without normalize (scale), Cross Validation Accuracy = 87%
with scale (-1 1), Cross Validation Accuracy = 92%
with parameter selection –c 2 –g 2, Cross Validation Accuracy = 95%

To get the probability information, in training part, use:
svm-train.exe -c 2 -g 2 -b 1 TrainingData.scale
In test/run part, make "int predict_probability=1;" and the program will call "predict_label = svm_predict_probability(model,x,prob_estimates);" , which writes the probability value in the output file.

[update:8/29/2011]:
To run step 3 successfully, make sure that you have that grid.py in the same directory, and in grid.py line 22:
make sure gnuplot installed in correct location, and svmtrain_exe is in correct directory too (may copy to a 'window' folder in current directory).
In file TrainingData.scale.model, take a look at 'label'. It could be 6 2 3 4 5 1, instead of 1 2 3 4 5 6