def testMFCC():
'''Does a simple test by comparing which of MFCC-SG + Kullback-Leibler distance or
MFCC-mean/var + euclidean distance gives the best results.'''
ds = loadDataSet()
ds = utils.addVarFromCov(ds, 'mfcc')
groundTruth = utils.getGroundTruthFromLabel(ds, 'genre')
print 'Evaluating 1-NN genre classification using euclidean distance on mfcc.mean and mfcc.var:'
confusion = nnclassifier.evaluate_1NN(
ds, groundTruth, 'euclidean',
{'descriptorNames': ['mfcc.mean', 'mfcc.var']})
print confusion.results()
cmfile = join(tempdir, 'confusion_meanvar.html')
open(cmfile, 'w').write(confusion.toHtml())
print '(wrote confusion matrix to %s)' % cmfile
print
print 'Evaluating 1-NN genre classification using Kullback-Leibler distance on mfcc:'
confusion = nnclassifier.evaluate_1NN(ds, groundTruth, 'kullbackleibler',
{'descriptorName': 'mfcc'})
print confusion.results()
cmfile = join(tempdir, 'confusion_singlegaussian.html')
open(cmfile, 'w').write(confusion.toHtml())
print '(wrote confusion matrix to %s)' % cmfile
print
def testMFCC():
'''Does a simple test by comparing which of MFCC-SG + Kullback-Leibler distance or
MFCC-mean/var + euclidean distance gives the best results.'''
ds = loadDataSet()
ds = utils.addVarFromCov(ds, 'mfcc')
groundTruth = utils.getGroundTruthFromLabel(ds, 'genre')
print 'Evaluating 1-NN genre classification using euclidean distance on mfcc.mean and mfcc.var:'
confusion = nnclassifier.evaluate_1NN(ds, groundTruth,
'euclidean',
{ 'descriptorNames': [ 'mfcc.mean', 'mfcc.var' ] })
print confusion.results()
cmfile = join(tempdir, 'confusion_meanvar.html')
open(cmfile, 'w').write(confusion.toHtml())
print '(wrote confusion matrix to %s)' % cmfile
print
print 'Evaluating 1-NN genre classification using Kullback-Leibler distance on mfcc:'
confusion = nnclassifier.evaluate_1NN(ds, groundTruth,
'kullbackleibler',
{ 'descriptorName': 'mfcc' })
print confusion.results()
cmfile = join(tempdir, 'confusion_singlegaussian.html')
open(cmfile, 'w').write(confusion.toHtml())
print '(wrote confusion matrix to %s)' % cmfile
print
def testClassify():
ds = loadDataSet()
# get ground truth
groundTruth = utils.getGroundTruthFromLabel(ds, 'genre')
ds_genre = transform(ds, 'select', {'descriptorNames': 'genre'})
ds_mfcc = transform(ds, 'select', {'descriptorNames': 'mfcc*'})
# transform dataset
ds = transform(ds, 'select', {'descriptorNames': ['spectral*', 'genre']})
ds_base = transform(ds, 'fixlength', {'descriptorNames': '*'})
ds = transform(ds_base, 'gaussianize')
ds = transform(ds, 'rca', {'dimension': 10, 'classLabel': 'genre'})
ds = mergeDataSets(ds, ds_genre)
ds = mergeDataSets(ds, ds_mfcc)
# launch classification/evaluation
from nnclassifier import evaluate_1NN
results = []
alpha = 0.0
while alpha <= 1.0:
print 'alpha =', alpha
confusion = evaluate_1NN(
ds, groundTruth, 'linearcombination', {
'mfcc_kl': {
'name': 'kullbackleibler',
'weight': alpha,
'params': {
'descriptorName': 'mfcc'
}
},
'spectral_euclidean': {
'name': 'euclidean',
'weight': 1 - alpha,
'params': {
'descriptorNames': 'rca*'
}
}
})
good = confusion.correct()
total = confusion.total()
print 'correctly classified:', good, 'out of', total, '(%d%%)' % int(
100 * good / total)
results.append(confusion.correct())
alpha += 0.1
# display results
plotResults = False
if plotResults:
import pylab
pylab.plot(results)
pylab.show()
def testClassify():
ds = loadDataSet()
# get ground truth
groundTruth = utils.getGroundTruthFromLabel(ds, 'genre')
ds_genre = transform(ds, 'select', { 'descriptorNames': 'genre' })
ds_mfcc = transform(ds, 'select', { 'descriptorNames': 'mfcc*' })
# transform dataset
ds = transform(ds, 'select', { 'descriptorNames': [ 'spectral*', 'genre' ] })
ds_base = transform(ds, 'fixlength', { 'descriptorNames': '*' })
ds = transform(ds_base, 'gaussianize')
ds = transform(ds, 'rca', { 'dimension': 10, 'classLabel': 'genre' })
ds = mergeDataSets(ds, ds_genre)
ds = mergeDataSets(ds, ds_mfcc)
# launch classification/evaluation
from nnclassifier import evaluate_1NN
results = []
alpha = 0.0
while alpha <= 1.0:
print 'alpha =', alpha
confusion = evaluate_1NN(ds, groundTruth, 'linearcombination',
{ 'mfcc_kl': { 'name': 'kullbackleibler',
'weight': alpha,
'params': { 'descriptorName': 'mfcc' } },
'spectral_euclidean': { 'name': 'euclidean',
'weight': 1-alpha,
'params': { 'descriptorNames': 'rca*' } }
})
good = confusion.correct()
total = confusion.total()
print 'correctly classified:', good, 'out of', total, '(%d%%)' % int(100*good/total)
results.append(confusion.correct())
alpha += 0.1
# display results
plotResults = False
if plotResults:
import pylab
pylab.plot(results)
pylab.show()
