def run(self):
super(ModFusionHistogram, self).run()
speech_frames_count = 0.0
noise_frames_count = 0.0
# initialize histograms
speech_histogram = {}
noise_histogram = {}
lr_histogram = {}
probabilites = {}
vad_combinations = product([0, 1], repeat=len(self.inputs))
for comb_vec in vad_combinations:
speech_histogram[comb_vec] = COMB_MIN_VALUE
noise_histogram[comb_vec] = COMB_MIN_VALUE
lr_histogram[comb_vec] = COMB_MIN_VALUE
probabilites[comb_vec] = COMB_MIN_VALUE
for element in self.vadpy.pipeline:
lo_list = [] # labels object aka "lo"
for attr in self.inputs:
lo_list.append(getattr(element, attr))
equalize_framelen(*(lo_list + [element.gt_labels]))
frame_len = lo_list[0].frame_len
frames_count = min(len(labels) for labels in lo_list)
for i in range(0, frames_count):
# i-th frame, (start, end, --> speech <-- ) tuple
comb_vec = tuple(int(lo[i][2]) for lo in lo_list)
probabilites[comb_vec] += 1
if element.gt_labels[i][2]:
speech_histogram[comb_vec] += 1
speech_frames_count += 1
else:
noise_histogram[comb_vec] += 1
noise_frames_count += 1
# normalize histograms
for key in probabilites:
speech_histogram[key] /= speech_frames_count
noise_histogram[key] /= noise_frames_count
probabilites[key] /= (speech_frames_count + noise_frames_count)
speech_val = speech_histogram[key]
noise_val = noise_histogram[key]
if noise_val == 0 or speech_val == 0:
lr_histogram[key] = 0.0
elif noise_val == 0:
lr_histogram[key] = 1.0
else:
lr_histogram[key] = speech_val / noise_val
# update pipeline with histogram data
self.add_result('speech', speech_histogram)
self.add_result('noise', noise_histogram)
self.add_result('lr', lr_histogram)
self.add_result('p', probabilites)
def run(self):
super(ModFusionHistogram, self).run()
speech_frames_count = 0.0
noise_frames_count = 0.0
# initialize histograms
speech_histogram = {}
noise_histogram = {}
lr_histogram = {}
probabilites = {}
vad_combinations = product([0,1], repeat = len(self.inputs))
for comb_vec in vad_combinations:
speech_histogram[comb_vec] = COMB_MIN_VALUE
noise_histogram[comb_vec] = COMB_MIN_VALUE
lr_histogram[comb_vec] = COMB_MIN_VALUE
probabilites[comb_vec] = COMB_MIN_VALUE
for element in self.vadpy.pipeline:
lo_list = [] # labels object aka "lo"
for attr in self.inputs:
lo_list.append(getattr(element, attr))
equalize_framelen(*(lo_list + [element.gt_labels]))
frame_len = lo_list[0].frame_len
frames_count = min(len(labels) for labels in lo_list)
for i in range(0, frames_count):
# i-th frame, (start, end, --> speech <-- ) tuple
comb_vec = tuple(int(lo[i][2]) for lo in lo_list)
probabilites[comb_vec] += 1
if element.gt_labels[i][2]:
speech_histogram[comb_vec] += 1
speech_frames_count += 1
else:
noise_histogram[comb_vec] += 1
noise_frames_count += 1
# normalize histograms
for key in probabilites:
speech_histogram[key] /= speech_frames_count
noise_histogram[key] /= noise_frames_count
probabilites[key] /= (speech_frames_count + noise_frames_count)
speech_val = speech_histogram[key]
noise_val = noise_histogram[key]
if noise_val == 0 or speech_val == 0:
lr_histogram[key] = 0.0
elif noise_val == 0:
lr_histogram[key] = 1.0
else:
lr_histogram[key] = speech_val / noise_val
# update pipeline with histogram data
self.add_result('speech', speech_histogram)
self.add_result('noise', noise_histogram)
self.add_result('lr', lr_histogram)
self.add_result('p', probabilites)
def run(self):
super(ModCorrelation, self).run()
assert len(set(self.inputs)) == 3, 'Q-statistics module requires three different inputs (GT, VAD1, VAD2)'
# false positives/false negatives per source
# the format of every tuple in dictionary is
# [True Positives, True Negatives, False Positives, False Negatives] tuple (list actually :)
a_total = 0.0
b_total = 0.0
c_total = 0.0
d_total = 0.0
for element in self.vadpy.pipeline:
gt_labels = getattr(element, self.inputs[0])
vad1_labels = getattr(element, self.inputs[1])
vad2_labels = getattr(element, self.inputs[2])
if len(set([len(gt_labels), len(vad1_labels), len(vad2_labels)])) != 1:
log.warning('Labels length mismatch: {0} / {1} / {2}, equlizing frame lengths.'.format(
len(gt_labels), len(vad1_labels), len(vad2_labels) ))
equalize_framelen(gt_labels, vad1_labels, vad2_labels)
speechData = zip((int(speech) for start, stop, speech in gt_labels),
(int(speech) for start, stop, speech in vad1_labels),
(int(speech) for start, stop, speech in vad2_labels))
a = 0; b = 0; c = 0; d = 0;
for i in range(0, len(speechData)):
valGT = speechData[i][0]
valV1 = speechData[i][1]
valV2 = speechData[i][2]
if valV1 == valGT and valV2 == valGT:
a += 1
elif valV1 != valGT and valV2 == valGT:
b += 1
elif valV1 == valGT and valV2 != valGT:
c += 1
else:
d += 1
a_total += a
b_total += b
c_total += c
d_total += d
length = a_total + b_total + c_total + d_total;
a = a_total / length
b = b_total / length
c = c_total / length
d = d_total / length
corrQ = (a*d - b*c) / (a*d + b*c)
corrp = (a*d - b*c) / math.sqrt((a + b)*(c + d)*(a + c)*(b + d))
self.add_result('corrQ', corrQ)
self.add_result('corrp', corrp)
def run(self):
super(ModCorrelation, self).run()
assert len(
set(self.inputs)
) == 3, 'Q-statistics module requires three different inputs (GT, VAD1, VAD2)'
# false positives/false negatives per source
# the format of every tuple in dictionary is
# [True Positives, True Negatives, False Positives, False Negatives] tuple (list actually :)
a_total = 0.0
b_total = 0.0
c_total = 0.0
d_total = 0.0
for element in self.vadpy.pipeline:
gt_labels = getattr(element, self.inputs[0])
vad1_labels = getattr(element, self.inputs[1])
vad2_labels = getattr(element, self.inputs[2])
if len(set([len(gt_labels),
len(vad1_labels),
len(vad2_labels)])) != 1:
log.warning(
'Labels length mismatch: {0} / {1} / {2}, equlizing frame lengths.'
.format(len(gt_labels), len(vad1_labels),
len(vad2_labels)))
equalize_framelen(gt_labels, vad1_labels, vad2_labels)
speechData = zip(
(int(speech) for start, stop, speech in gt_labels),
(int(speech) for start, stop, speech in vad1_labels),
(int(speech) for start, stop, speech in vad2_labels))
a = 0
b = 0
c = 0
d = 0
for i in range(0, len(speechData)):
valGT = speechData[i][0]
valV1 = speechData[i][1]
valV2 = speechData[i][2]
if valV1 == valGT and valV2 == valGT:
a += 1
elif valV1 != valGT and valV2 == valGT:
b += 1
elif valV1 == valGT and valV2 != valGT:
c += 1
else:
d += 1
a_total += a
b_total += b
c_total += c
d_total += d
length = a_total + b_total + c_total + d_total
a = a_total / length
b = b_total / length
c = c_total / length
d = d_total / length
corrQ = (a * d - b * c) / (a * d + b * c)
corrp = (a * d - b * c) / math.sqrt(
(a + b) * (c + d) * (a + c) * (b + d))
self.add_result('corrQ', corrQ)
self.add_result('corrp', corrp)
def run(self):
super(ModConfusion, self).run()
assert len(set(self.inputs)) == 2, 'Confusion module requires two different inputs'
# false positives/false negatives per source
# the format of every tuple in dictionary is
# [True Positives, True Negatives, False Positives, False Negatives] tuple (list actually :)
tp_total = 0.0
tn_total = 0.0
fp_total = 0.0
fn_total = 0.0
for element in self.vadpy.pipeline:
# Generate a list of decision (speech/noise) pairs for Labels objects
#
gt_labels = getattr(element, self.inputs[0])
vad_labels = getattr(element, self.inputs[1])
if len(gt_labels) != len(vad_labels):
log.warning('Labels length mismatch: {0} / {1}, equlizing frame lengths.'.format(
len(gt_labels), len(vad_labels)))
equalize_framelen(gt_labels, vad_labels)
# zip will concatenate up to min. length of the objects
speechAB = zip((int(speech) for start, stop, speech in gt_labels),
(int(speech) for start, stop, speech in vad_labels))
# Calculate False alarm and Miss rate
tp = 0; tn = 0; fp = 0; fn = 0;
for i in range(0, len(speechAB)):
valA = speechAB[i][0]
if (self.ctx_size < i < len(speechAB) - self.ctx_size):
valB = int(round(sum(vAB[1] for vAB in speechAB[i - self.ctx_size :
i + self.ctx_size + 1])
/ float(self.ctx_size * 2 + 1)))
else:
valB = speechAB[i][1]
if valA: # concluding, valA is a value 'Speech' Ground Truth frame
if valB: tp += 1 # true positive
else: fn += 1 # false negative, miss
else:
if valB: fp += 1 # false positive, false alarm
else: tn += 1 # true negative
tp_total += tp
tn_total += tn
fp_total += fp
fn_total += fn
tp = tp_total
tn = tn_total
fp = fp_total
fn = fn_total
length = tp + tn + fp + fn;
gt_speech = fn + tp
gt_noise = fp + tn
mr = fn / (tp + fn)
far = fp / (tn + fp)
#total_len = tn + fn + tp + fp
self.add_result('mr', mr)
self.add_result('far', far)
def run(self):
super(ModConfusion, self).run()
assert len(set(self.inputs)
) == 2, 'Confusion module requires two different inputs'
# false positives/false negatives per source
# the format of every tuple in dictionary is
# [True Positives, True Negatives, False Positives, False Negatives] tuple (list actually :)
tp_total = 0.0
tn_total = 0.0
fp_total = 0.0
fn_total = 0.0
for element in self.vadpy.pipeline:
# Generate a list of decision (speech/noise) pairs for Labels objects
#
gt_labels = getattr(element, self.inputs[0])
vad_labels = getattr(element, self.inputs[1])
if len(gt_labels) != len(vad_labels):
log.warning(
'Labels length mismatch: {0} / {1}, equlizing frame lengths.'
.format(len(gt_labels), len(vad_labels)))
equalize_framelen(gt_labels, vad_labels)
# zip will concatenate up to min. length of the objects
speechAB = zip((int(speech) for start, stop, speech in gt_labels),
(int(speech) for start, stop, speech in vad_labels))
# Calculate False alarm and Miss rate
tp = 0
tn = 0
fp = 0
fn = 0
for i in range(0, len(speechAB)):
valA = speechAB[i][0]
if (self.ctx_size < i < len(speechAB) - self.ctx_size):
valB = int(
round(
sum(vAB[1]
for vAB in speechAB[i - self.ctx_size:i +
self.ctx_size + 1]) /
float(self.ctx_size * 2 + 1)))
else:
valB = speechAB[i][1]
if valA: # concluding, valA is a value 'Speech' Ground Truth frame
if valB: tp += 1 # true positive
else: fn += 1 # false negative, miss
else:
if valB: fp += 1 # false positive, false alarm
else: tn += 1 # true negative
tp_total += tp
tn_total += tn
fp_total += fp
fn_total += fn
tp = tp_total
tn = tn_total
fp = fp_total
fn = fn_total
length = tp + tn + fp + fn
gt_speech = fn + tp
gt_noise = fp + tn
mr = fn / (tp + fn)
far = fp / (tn + fp)
#total_len = tn + fn + tp + fp
self.add_result('mr', mr)
self.add_result('far', far)
