def cook():
for wavfilename in glob(join(COOKED_FOLDER, '*.wav')):
print('Cooking ' + split(wavfilename)[1])
signal = Signal(wavfilename)
feat = mfcc(signal.data, signal.rate)
outname = MFCC_DIR + split(wavfilename)[1].replace('wav', 'txt')
out = open(outname, 'w')
# out.write(str(len(feat))+' '+str(len(feat[0]))+'\n')
for i in range(len(feat)):
out.write(str(list(feat[i])).strip('[]').replace(' ', '') + '\n')
out.close()
print('Done')
def inputs(signal): signal_fft = zeros(256) i = 0 while(i + 400 < len(signal)): signal_fft = vstack([signal_fft, frame_fft(signal[i : i+400], window = 'Hamming', K = 512, N = 400, size1 = 256)]) i += 160 signal_fft = delete(signal_fft, 0, axis = 0) MFCC = zeros(13) for i in signal_fft: MFCC = vstack([MFCC, mfcc(i)]) MFCC = delete( MFCC, 0, axis = 0) return MFCC
def create_json():
(rate, sig) = wav.read("english.wav")
mfcc_feat = mfcc(sig, rate)
d_mfcc_feat = delta(mfcc_feat, 2)
fbank_feat = logfbank(sig, rate)
python_arr = fbank_feat[1:3, :]
print(python_arr)
json_conv = python_arr.tolist()
database_format = json.dumps({
"Chris": json_conv
}) # Need to change to the user inputed name in the application
#with open('coeff2.json', 'w') as json_file:
#json_file.write(database_format)
#os.system("mv coeff.json newfile.json")
return render_template('main.html', dblist=database_format)
def main():
n = 1
step = 15
width = 30
Mono, fs = normalize('../../audio/cmu_us_bdl_arctic/wav/arctic_a0001.wav')
ms = makeframe(Mono, width, step, fs)
# check Preprocessing.py
pitch(n)
# check Pitch.py
y = formant(fs, Mono)
# check Formants.py
y = mfcc(fs, Mono)
# check MFCC.py
nbrf = 15 #nbr de fichier aléatoires sélectionnés pour le rule-based system
rulebased(nbrf)
#!/usr/bin/env python
from MFCC import mfcc
from MFCC import delta
from MFCC import logfbank
import scipy.io.wavfile as wav
(rate,sig) = wav.read("../sounds/english.wav")
mfcc_feat = mfcc(sig, rate)
d_mfcc_feat = delta(mfcc_feat, 2)
fbank_feat = logfbank(sig, rate)
print(fbank_feat[1:3,:])
while i < len(signal)-100:
# print i
object = part(i, signal)
i = object.end
simplelist.append(object)
print "end"
global_max = max( map( lambda x: x.maxima, simplelist))
new_list = filter( lambda x: x.maxima >= float(global_max)/16, simplelist)
print len(new_list), len(simplelist)
sig = []
for i in new_list:
sig = concatenate([sig, signal[i.start:i.end]])
# write("edited.wav", rate, sig)
signal_fft = zeros(256)
i = 0
while(i + 400 < len(signal)):
signal_fft = vstack([signal_fft, frame_fft(signal[i : i+400], window = 'Hamming', K = 512, N = 400, size1 = 256)])
i += 160
signal_fft = delete(signal_fft, 0, axis = 0)
MFCC = zeros(13)
for i in signal_fft:
MFCC = vstack([MFCC, mfcc(i)])
MFCC = delete( MFCC, 0, axis = 0)
savetxt("MFCC.csv", MFCC, delimiter = ',')
