def features_from_base(basepath, order=0):
(females, males) = read_speakers(basepath)
# list of list (sorted)
female_utterances_list = [
read_utterances(basepath, female) for female in females
]
male_utterances_list = [read_utterances(basepath, male) for male in males]
# utterances as Wave objects
female_utterances_list = read_utterances_files(basepath,
female_utterances_list, 'f')
male_utterances_list = read_utterances_files(basepath,
male_utterances_list, 'm')
for utterances in female_utterances_list:
for utterance in utterances:
uttMFCCs = features.mfcc(utterance.signal,
samplerate=utterance.sample_rate,
numcep=19,
highfreq=utterance.sample_rate / 2)
if (order > 0):
uttMFCCs = features.appendDeltasAllFrames(uttMFCCs, order)
print(uttMFCCs.shape)
print(uttMFCCs)
print()
def features_from_base(basepath, order=0):
(females, males) = read_speakers(basepath)
# list of list (sorted)
female_utterances_list = [read_utterances(basepath, female) for female in females]
male_utterances_list = [read_utterances(basepath, male) for male in males]
# utterances as Wave objects
female_utterances_list = read_utterances_files(basepath, female_utterances_list, 'f')
male_utterances_list = read_utterances_files(basepath, male_utterances_list, 'm')
for utterances in female_utterances_list:
for utterance in utterances:
uttMFCCs = features.mfcc(utterance.signal, samplerate=utterance.sample_rate,
numcep=19, highfreq=utterance.sample_rate/2)
if(order > 0):
uttMFCCs = features.appendDeltasAllFrames(uttMFCCs, order)
print(uttMFCCs.shape)
print(uttMFCCs)
print()
numcep=19,
highfreq=utterance.sample_rate / 2)
if (order > 0):
uttMFCCs = features.appendDeltasAllFrames(uttMFCCs, order)
print(uttMFCCs.shape)
print(uttMFCCs)
print()
def enroll_1():
features_from_base(ENROLL_1, order=2)
# TODO fazer o mesmo para enroll_2 e imposter
if __name__ == '__main__':
enroll_1()
wave = Wave('test.wav')
framedMFCCs = features.mfcc(wave.signal,
samplerate=wave.sample_rate,
numcep=19,
highfreq=wave.sample_rate / 2)
framedMFCCsDelta = features.appendDeltasAllFrames(framedMFCCs)
framedMFCCsDeltaDelta = features.appendDeltasAllFrames(framedMFCCs,
order=2)
print('framedMFCCs:', framedMFCCs.shape)
print('framedMFCCs + delta:', framedMFCCsDelta.shape)
print('framedMFCCs + delta + delta-delta:', framedMFCCsDeltaDelta.shape)
male_utterances_list = read_utterances_files(basepath, male_utterances_list, 'm')
for utterances in female_utterances_list:
for utterance in utterances:
uttMFCCs = features.mfcc(utterance.signal, samplerate=utterance.sample_rate,
numcep=19, highfreq=utterance.sample_rate/2)
if(order > 0):
uttMFCCs = features.appendDeltasAllFrames(uttMFCCs, order)
print(uttMFCCs.shape)
print(uttMFCCs)
print()
def enroll_1():
features_from_base(ENROLL_1, order=2)
# TODO fazer o mesmo para enroll_2 e imposter
if __name__ == '__main__':
enroll_1()
wave = Wave('test.wav')
framedMFCCs = features.mfcc(wave.signal, samplerate=wave.sample_rate,
numcep=19, highfreq=wave.sample_rate/2)
framedMFCCsDelta = features.appendDeltasAllFrames(framedMFCCs)
framedMFCCsDeltaDelta = features.appendDeltasAllFrames(framedMFCCs, order=2)
print('framedMFCCs:', framedMFCCs.shape)
print('framedMFCCs + delta:', framedMFCCsDelta.shape)
print('framedMFCCs + delta + delta-delta:', framedMFCCsDeltaDelta.shape)
