def test(self):
expected = array([1,13,21,25])
print(self.mfcc.shape)
segStart = to_segments(self.mfcc.T)
print(segStart)
print(expected)
self.assertEqual(segStart,expected)
def analyze_config(self,config):
cache = {}
num_bands=config.num_bands.get_value()
freq_lims = (config.min_freq.get_value(),config.max_freq.get_value())
window_length = config.window_length.get_value()
time_step = config.time_step.get_value()
use_praat = config.use_praat.get_value()
num_coeffs = config.num_coeffs.get_value()
num_bands = config.num_bands.get_value()
use_window = config.use_window.get_value()
use_power = config.use_power.get_value()
use_segments = config.use_segments.get_value()
if config.representation == 'envelopes':
if use_window:
to_rep = partial(to_envelopes,
num_bands=num_bands,
freq_lims=freq_lims,
window_length=window_length,
time_step=time_step)
else:
to_rep = partial(to_envelopes,num_bands=num_bands,freq_lims=freq_lims)
elif config.representation == 'mfcc':
if use_praat:
to_rep = partial(to_mfcc_praat, freq_lims=freq_lims,
num_coeffs=num_coeffs,
win_len=window_length,
time_step=time_step)
else:
to_rep = partial(to_mfcc,freq_lims=freq_lims,
num_coeffs=num_coeffs,
num_filters = num_bands,
win_len=window_length,
time_step=time_step,
use_power = use_power)
elif config.representation == 'mhec':
to_rep = partial(to_mhec, freq_lims=freq_lims,
num_coeffs=num_coeffs,
num_filters = num_bands,
window_length=window_length,
time_step=time_step,
use_power = use_power)
elif config.representation == 'gammatone':
if use_window:
to_rep = partial(to_gammatone_envelopes,num_bands = num_bands,
freq_lims=freq_lims,
window_length=window_length,
time_step=time_step)
else:
to_rep = partial(to_gammatone_envelopes,num_bands = num_bands,
freq_lims=freq_lims)
elif config.representation == 'melbank':
to_rep = partial(to_melbank,freq_lims=freq_lims,
win_len=window_length,
time_step=time_step,
num_filters = num_bands)
elif config.representation == 'prosody':
to_rep = partial(to_prosody,time_step=time_step)
if config.match_algorithm == 'xcorr':
dist_func = xcorr_distance
elif config.match_algorithm == 'dtw':
dist_func = dtw_distance
elif config.match_algorithm == 'dct':
dist_func = dct_distance
x = []
y = []
for pm in self.mapping:
listenertup = tuple(map(lambda x: os.path.split(x)[1],pm))
if listenertup not in self.listenerResp:
continue
if pm[0] not in cache:
cache[pm[0]] = to_rep(pm[0])
if use_segments and cache[pm[0]] is not None:
cache[pm[0]] = to_segments(cache[pm[0]])
if pm[1] not in cache:
cache[pm[1]] = to_rep(pm[1])
if use_segments and cache[pm[1]] is not None:
cache[pm[1]] = to_segments(cache[pm[1]])
if pm[2] not in cache:
cache[pm[2]] = to_rep(pm[2])
if use_segments and cache[pm[2]] is not None:
cache[pm[2]] = to_segments(cache[pm[2]])
base = cache[pm[0]]
model = cache[pm[1]]
shadow = cache[pm[2]]
if base is None:
continue
if model is None:
continue
if shadow is None:
continue
dist1 = dist_func(base,model)
if dist1 == 0 or isnan(dist1):
print(base)
print(model)
print(dist1)
print(dist2)
raise(ValueError)
dist2 = dist_func(shadow,model)
if isnan(dist2):
print(base)
print(model)
print(dist1)
print(dist2)
raise(ValueError)
if config.use_similarity:
dist1 = 1/dist1
dist2 = 1/dist2
ratio = dist2 / dist1
x.append(self.listenerResp[listenertup])
y.append(ratio)
correlation = pearsonr(x,y)
return correlation[0]
