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 descnames(config, name):
dnames = '%s descriptors' % name
result = config[dnames]
if config[dnames + ' except']:
result += ', except %s' % config[dnames + ' except']
return result
if __name__ == '__main__':
cvar.verbose = False
if config['dataset'] == 'dataset':
ds = loadDataSet()
ds = utils.addVarFromCov(ds, 'mfcc')
elif config['dataset'] == 'dortmund':
ds = loadDortmundDataSet()
ds = utils.addVarFromCov(ds, 'lowlevel.mfcc')
else:
print 'Unknown dataset:', config['dataset']
groundTruth = utils.getGroundTruthFromLabel(ds, 'genre')
# NN-classifier: euclidean distance on MFCC mean/var
if 'baseline' in config['classifiers']:
print hbar
print 'Evaluating 1-NN genre classification using euclidean distance on MFCC.mean and MFCC.var:'
def descnames(config, name):
dnames = '%s descriptors' % name
result = config[dnames]
if config[dnames + ' except']:
result += ', except %s' % config[dnames + ' except']
return result
if __name__ == '__main__':
cvar.verbose = False
if config['dataset'] == 'dataset':
ds = loadDataSet()
ds = utils.addVarFromCov(ds, 'mfcc')
elif config['dataset'] == 'dortmund':
ds = loadDortmundDataSet()
ds = utils.addVarFromCov(ds, 'lowlevel.mfcc')
else:
print 'Unknown dataset:', config['dataset']
groundTruth = utils.getGroundTruthFromLabel(ds, 'genre')
# NN-classifier: euclidean distance on MFCC mean/var
if 'baseline' in config['classifiers']:
print hbar
print 'Evaluating 1-NN genre classification using euclidean distance on MFCC.mean and MFCC.var:'
'''
