# np.var(map(lambda x: (mel[x,i] - moy[j]) / np.sqrt(var[j]), times))
features["moy_loc_%s" %j] =\
np.mean(map(lambda x: (mel[x,i] - mel_min) / (mel_max - mel_min), times))
features["var_loc_%s" %j] =\
np.var(map(lambda x: (mel[x,i] - mel_min) / (mel_max - mel_min), times))
try:
text += words[2]+'\t'+str(features)+'\n'
except BaseException, e:
print e
return text
f = open('transcript_test', 'Ur')
text = f.read()
turns = text.split('\n.\n')
turn = turns[1]
turn_formated = turn.split('\n')[1:]
dump = read_turn(mel, moy, var, turn_formated)
#%% Fourth step : read a file
def normalize_signal(path1, path2):
"""attention, retourne directement le tableau du signal."""
f = Sndfile(path1)
n = int(f.nframes)
fs = f.samplerate
nc = f.channels # checker que c'est bien 1
if nc != 1:
