def test_find_dict_by_item(self):
assert_equal(
find_dict_by_item(('duration', 3), self.dicts),
{'duration': 3, 'ss': 4}
)
assert_is_none(
find_dict_by_item(('duration', 5), self.dicts)
)
def extract_features(dataset_id, instance_id, audiofile_path):
dataset = Dataset.objects.get(pk=dataset_id)
inst = Instance.objects.get(pk=instance_id)
n_frames, sample_rate, duration = 0, 0, 0
# Calculate the sample rate and duration
with contextlib.closing(wave.open(audiofile_path, 'r')) as audiofile:
n_frames = audiofile.getnframes()
sample_rate = audiofile.getframerate()
duration = n_frames / float(sample_rate)
# Format - {'Display name': 'name: Definition'}
FEATURES = [
{'display_name': 'Spectral Shape Characteristics',
'yaafe_name': 'sss',
'yaafe_definition': 'SpectralShapeStatistics',
'subfeatures': ['Spectral centroid', 'Spectral spread', 'Spectral kurtosis', 'Spectral skewness']
},
{'display_name': 'Temporal Shape Characteristics',
'yaafe_name': 'tss',
'yaafe_definition': 'TemporalShapeStatistics',
'subfeatures': ['Temporal centroid', 'Temporal spread', 'Temporal kurtosis', 'Temporal skewness']
},
{'display_name': 'ZCR',
'yaafe_name': 'zcr',
'yaafe_definition': 'ZCR',
'unit': 'Hz'
},
{'display_name': 'Energy',
'yaafe_name': 'energy',
'yaafe_definition': 'Energy',
},
{'display_name': 'Loudness',
'yaafe_name': 'loudness',
'yaafe_definition': 'Loudness',
},
{'display_name': 'Spectral rolloff',
'yaafe_name': 'spectral_rolloff',
'yaafe_definition': 'SpectralRolloff',
},
{'display_name': 'Perceptual sharpness',
'yaafe_name': 'perceptual_sharpness',
'yaafe_definition': 'PerceptualSharpness',
},
{'display_name': 'Perceptual spread',
'yaafe_name': 'perceptual_spread',
'yaafe_definition': 'PerceptualSpread',
},
{'display_name': 'Duration',
'unit': 's',
},
{'display_name': 'Sample rate',
'unit': 'Hz',
},
{'display_name': 'Spectral decrease',
'yaafe_name': 'spectral_decrease',
'yaafe_definition': 'SpectralDecrease',
},
{'display_name': "Spectral flatness",
'yaafe_name': 'spectral_flatness',
'yaafe_definition': 'SpectralFlatness',
},
# {'display_name': "Spectral flux",
# 'yaafe_name': 'spectral_flux',
# 'yaafe_definition': 'SpectralFlux',
# },
{'display_name': "Spectral slope",
'yaafe_name': 'spectral_slope',
'yaafe_definition': 'SpectralSlope',
},
# {'display_name': "Spectral variation",
# 'yaafe_name': 'spectral_variation',
# 'yaafe_definition': 'SpectralVariation',
# }
]
# Add features to extract
feature_plan = yf.FeaturePlan(sample_rate=sample_rate, resample=False)
for feature in FEATURES:
if 'yaafe_definition' in feature:
# YAAFE feature plans take definitions of the form: 'zcr: ZCR'
full_definition = feature['yaafe_name'] + ': ' + feature['yaafe_definition']
# Add the feature to the feature plan to be extracted
feature_plan.addFeature(full_definition)
# Configure an Engine
engine = yf.Engine()
engine.load(feature_plan.getDataFlow())
# Extract features
afp = yf.AudioFileProcessor()
afp.processFile(engine, audiofile_path)
# outputs dict format - {'Spectral centroid': [[2.33], [4.34],...[2.55]]}
outputs = {}
# Read and store output arrays to outputs dict
for feature in FEATURES:
if 'yaafe_definition' in feature: # Exclude duration and sample rate
output_name = feature['yaafe_name']
# If the feature has subfeatures, e.g. Spec shape stats
if 'subfeatures' in feature:
full_output = engine.readOutput(output_name)
for i, subfeature_display_name in enumerate(feature['subfeatures']):
outputs[subfeature_display_name] = full_output[:, i]
# If the feature has only 1 dimension(1 X T array)
else:
display_name = feature['display_name']
a = engine.readOutput(output_name) # 2D array
# Transpose data to make it a 1D array
outputs[display_name] = a.transpose()[0]
# Create YAAFE feature objects
feature_obj_list = []
for display_name in outputs.keys():
feature = find_dict_by_item(('display_name', display_name), FEATURES)
f, created = Feature.objects.get_or_create(
name=display_name.lower(),
display_name=display_name
)
if feature and ('unit' in feature):
f.unit = feature['unit']
f.save()
feature_obj_list.append(f)
# Create Sample rate and Duration objects
rate_obj, created = Feature.objects.get_or_create(name='sample rate')
if not rate_obj.unit:
rate_obj.unit = 'Hz'
rate_obj.save()
feature_obj_list.append(rate_obj)
duration_obj, created = Feature.objects.get_or_create(name='duration')
if not duration_obj.unit:
duration_obj.unit = 's'
duration_obj.save()
feature_obj_list.append(duration_obj)
# Associate features with instance
# for feature in feature_obj_list:
# inst.features.add(feature)
# If dataset has labels
if dataset.labels():
# NOTE: This assumes there's only one label name per dataset.
# Just indexes the first label name
label_name = dataset.labels()[0]
else:
# attach a placeholder LabelName called 'variable'
filtered = LabelName.objects.filter(name='variable')
# make sure that 'get' doesn't return an error if there are more than 1
# LabelName called 'variable'
if len(filtered) <= 1:
label_name, c = LabelName.objects.get_or_create(name='variable')
else:
label_name = filtered[0]
# Add a placeholder label value called "none" to instance
# This is necessary in order for plotting to work
filtered = LabelValue.objects.filter(value="none", label_name=label_name)
if len(filtered) <= 1:
no_label, c = LabelValue.objects.get_or_create(value="none",
label_name=label_name)
else:
no_label = filtered[0]
inst.label_values.add(no_label)
inst.save()
# Save output data and associate it with inst
for display_name, output in outputs.iteritems():
if output.size > 0: # Avoid empty data
for i in range(output[0].size):
output_mean = output[i].mean()
FeatureValue.objects.create(value=output_mean,
feature=Feature.objects.get(name__iexact=display_name.lower()),
instance=inst)
# Save sample_rate and duration data
FeatureValue.objects.create(value=sample_rate,
feature=Feature.objects.get(name='sample rate'),
instance=inst)
FeatureValue.objects.create(value=duration,
feature=Feature.objects.get(name='duration'),
instance=inst)