def extract(filtered_audio, Fs):
nfft = 256
num_bins = 40
start_frequency = 150
end_frequency = 3200
c = filtered_audio.shape[0]
features = []
for i in range(c):
framed_audio = sigproc.framesig(filtered_audio[i], 256, 128)
features.append([])
j = framed_audio.shape[0]
if j > 10:
req_frames = framed_audio[int(j / 2) - 5:int(j / 2) + 5]
print(req_frames)
for k in range(len(req_frames)):
peak_amp, peak_freq = sp_peak_amp_freq.peakFreq(req_frames[k], 50)
pitch_periods = sp_pitch_period.pitch_period(req_frames[k], Fs)
form = formants.formant(req_frames[k])
cep = LPCC.lpcc(req_frames[k])
real_cc = RCC.rcc(req_frames[k])
lsfs = lsf.LSF(req_frames[k])
hjorth_parameters = hjorth.params(req_frames[k])
wavelet = dwt.wenergy(req_frames[k], 'db7', 5)
features[i].extend(lsfs)
features[i].extend(hjorth_parameters)
features[i].extend(wavelet)
return features
def create_from_options(cls, parser, options):
noptions = ((options.ipython_profile and 1 or 0) +
(options.lsf_directory and 1 or 0) +
(options.multiprocessing and 1 or 0))
if noptions > 1:
parser.error('You can only specify one of --ipython-profile, --lsf-directory, and --multiprocessing.')
if options.lsf_directory:
import lsf
return lsf.LSF(options.njobs, options.lsf_directory, memory=options.memory, job_array_name = options.jobname)
elif options.ipython_profile:
from IPython.parallel import Client, LoadBalancedView
client = Client(profile=options.ipython_profile)
return IPython(client)
elif options.multiprocessing:
return Multiprocessing()
else:
return Uniprocessing()
def extract(filtered_audio, Fs):
nfft = 256
num_bins = 40
start_frequency = 150
end_frequency = 3200
c = filtered_audio.shape[0]
features = []
for i in range(c):
features.append([])
peak_amp, peak_freq = sp_peak_amp_freq.peakFreq(filtered_audio[i], 50)
pitch_periods = sp_pitch_period.pitch_period(filtered_audio[i], Fs)
form = formants.formant(filtered_audio[i])
cep = LPCC.lpcc(filtered_audio[i])
real_cc = RCC.rcc(filtered_audio[i])
lsfs = lsf.LSF(filtered_audio[i])
hjorth_parameters = hjorth.params(filtered_audio[i])
wavelet = dwt.wenergy(filtered_audio[i], 'db7', 5)
features[i].extend(lsfs)
features[i].extend(hjorth_parameters)
features[i].extend(wavelet)
return features