def run_experiment_2(path=get_dropbox_path()+"yes-no-test/"):
"""
Run Experiment 2.
Use the Yes/No sample collection, but only the "Y" and "NONE" samples.
Use a single class classifier (for Yes).
Run the offline version of the SVM classifier.
Parameters:
-----------
path - string
the path to search for the samples
"""
print('Running Experiment 2')
# return a list of the audio test samples
samples = find_testsamples(path)
classes=["Y","NONE"]
sample_set = SampleSet(samples,classes=classes)
sample_set.stats()
(train,test) = sample_set.sample()
# generate features and classes from TestSamples
train_features = []
train_classes = []
for sample in train:
train_features.append(feature_factory(sample))
train_classes.append(single_class_factory(sample,"Y"))
#print(str(sample))
#print('class: '+str(single_class_factory(sample,"Y")))
# train the classifier
print("Training classifier...")
classifier = SVMClassifier()
classifier.train(train_features,train_classes)
print("Testing classifier...")
# Test samples one by one
score = 0
for sample in test:
prediction = classifier.predict(feature_factory(sample))
test_class = single_class_factory(sample,"Y")
if prediction != test_class:
print(str(sample))
print("incorrect classification (prediction: "+str(prediction)+", class: "+str(test_class)+")")
print("feature: "+str(feature_factory(sample)))
else:
print("correct classification")
score=score+1
print('Classification score: '+str(score)+'/'+str(len(test)))
print('Classification score: '+str(float(score)/float(len(test))))
def run_experiment_0(path=get_dropbox_path()+"old-test/"):
"""
Run Experiment 0.
Use the old simple vowel sample collection (only A and E present).
Use a single class classifier (for A).
Run the offline version of the Template Classifier.
Parameters:
-----------
path - string
the path to search for the Templates and TestRecordings.
"""
print('Running Experiment 0')
print('Running template matching')
classifier = TemplateClassifier()
# train classifier
templates = find_templates(path)
if len(templates) < 1:
raise ValueError('No templates found! aborting')
print("Training on template: "+str(templates[0]))
# train on a single template for now
classifier.train(templates[0])
# test a single recording with classifier
recordings = find_testrecordings(get_dropbox_path())
classifier.test_recording(recordings[0].path)
print "Test: "+str(recordings[0])
# plot results
plt.plot(classifier._get_raw_result())
plt.show()
print 'Classifier test times: '+str(classifier._get_raw_test_times())
print 'Classifier read times: '+str(classifier._get_raw_read_times())
def run_experiment_1(include_none=False,path=get_dropbox_path()+"yes-no-test/"):
"""
Run Experiment 1.
Use the Yes/No sample collection.
Use a single class classifier (for Yes).
Run the offline version of the SVM classifier.
Parameters:
-----------
include_none - bool
whether or not to include the "NONE" class samples
path - string
the path to search for the samples
"""
print('Running Experiment 1')
# return a list of the audio test samples
samples = find_testsamples(path)
classes=["Y","N"]
if include_none:
classes.append("NONE")
sample_set = SampleSet(samples,classes=classes)
sample_set.stats()
(train,test) = sample_set.sample()
# generate features and classes from TestSamples
train_features = []
train_classes = []
for sample in train:
train_features.append(feature_factory(sample))
train_classes.append(class_factory(sample))
print('class: '+str(class_factory(sample)))
# train the classifier
classifier = SVMClassifier()
classifier.train(train_features,train_classes)
# generate features and classes from TestSamples
test_features = []
test_classes = []
for sample in test:
test_features.append(feature_factory(sample))
test_classes.append(class_factory(sample))
# test the classifier
classifier.test(test_features,test_classes)
def train_offline_svm_test_live(path=get_dropbox_path()):
"""
Train the offline version of the SVM classifier on all
samples found in the path. Then run the classifier on
1 second inputs live.
Parameters
----------
path - string
The directory to search for TestSamples
"""
print('Training SVM classifier')
# return a list of the audio test samples
samples = find_testsamples(path)
sample_set = SampleSet(samples,classes=["Y","NONE"])
sample_set.stats()
(train,test) = sample_set.sample(in_order=False)
# generate features and classes from TestSamples
train_features = []
train_classes = []
for sample in train:
train_features.append(feature_factory(sample))
train_classes.append(class_factory(sample))
# train the classifier
classifier = SVMClassifier()
classifier.train(train_features,train_classes)
prompt ='Would you like to record a 3-sec sample to test?'
if raw_input(prompt) == 'y':
print('Recording now...')
live_recording = record_sample(samplerate=22050,blocking=True)
# process the recording into test samples
test_samples = process_live_recording(live_recording,"UChiApt",str(time.time()),22050)
print('Testing...')
for sample in test_samples:
feature = feature_factory(sample)
print("Prediction: "+str(classifier.predict(feature)))
else:
print('Exiting...')
# This contains a collection of short examples about how to use the new
# classes in the "dataset" Python module. Recall that a Python module is simply
# a collection of classes and functions within a single file.
from cssigps.dataset import *
from get_dropbox_path import *
if __name__ == '__main__':
print('Running example3 - process test recordings to generate new samples')
# generate samples for all of the current recordings
path=get_dropbox_path()+"simple-yes-no-test/"
recordings = find_testrecordings(path)
print('\nFound '+str(len(recordings))+' recordings')
if len(recordings) > 0:
prompt='This will generate new sample wav files for each test recording.\n Do you want to continue? (y/n)'
if raw_input(prompt) == 'y':
for recording in recordings:
print('\t'+str(recording))
recording.generate_samples('..'+os.sep+'samples'+os.sep)
else:
print('aborting...')
# generate features and classes from TestSamples
train_features = []
train_classes = []
for sample in train:
train_features.append(feature_factory(sample))
train_classes.append(class_factory(sample))
# train the classifier
classifier = SVMClassifier()
classifier.train(train_features,train_classes)
prompt ='Would you like to record a 3-sec sample to test?'
if raw_input(prompt) == 'y':
print('Recording now...')
live_recording = record_sample(samplerate=22050,blocking=True)
# process the recording into test samples
test_samples = process_live_recording(live_recording,"UChiApt",str(time.time()),22050)
print('Testing...')
for sample in test_samples:
feature = feature_factory(sample)
print("Prediction: "+str(classifier.predict(feature)))
else:
print('Exiting...')
if __name__ == '__main__':
print("Training the classifier against the Yes/No dataset")
path = get_dropbox_path()+"yes-no-test/"
train_offline_svm_test_live(path)
if __name__ == '__main__':
# decide which experiment to run based on the command line or user-input
response = ""
if len(sys.argv) >= 2:
response=sys.argv[1]
if response in ["-h","--help"]:
print_usage()
quit()
else:
prompt = "Which experiment would you like to run? [0-2]"
response = raw_input(prompt)
# run experiment
if response == "0":
path=get_dropbox_path()+"old-test/"
run_experiment_0(path)
elif response == "1":
run_experiment_1(include_none=True)
elif response == "2":
run_experiment_2()
elif response == "3":
run_experiment_3()
elif response == "4":
run_experiment_4()
elif response == "5":
path=get_dropbox_path()+"vowels-test/"
run_offline_svm(path)
elif response == "S":
# run single class classifier
c = sys.argv[2]
# This contains a collection of short examples about how to use the new
# classes in the "dataset" Python module. Recall that a Python module is simply
# a collection of classes and functions within a single file.
from cssigps.dataset import *
from cssigps.feature import *
from get_dropbox_path import *
if __name__ == '__main__':
print('Running example4 - generates features for each test sample')
path=get_dropbox_path()
# return a list of the audio test samples
samples = find_testsamples(path)
print('\nFound '+str(len(samples))+' samples')
# generate features
print("\nGenerating MFCCFeature")
for sample in samples:
if sample.env == 'Q1':
sample.check()
print('\t'+str(sample))
# feature = FreqBinFeature(sample)
feature = MFCCFeature(sample) #FreqBinFeature(sample)
feature.stats()
break
