def __init__(self,
sample_rate=44100,
normalize=None,
resample=False,
time_start=0.0,
time_limit=0.0):
if type(normalize) == int:
normalize = '%i' % normalize
elif type(normalize) == float:
normalize = '%f' % normalize
elif normalize and type(normalize) != str:
normalize = str(normalize)
self.features = {}
self.resample = resample
self.sample_rate = sample_rate
self.audio_params = {
'SampleRate': str(sample_rate),
'Resample': 'yes' if resample else 'no',
'TimeStart': '%ss' % time_start,
'TimeLimit': '%ss' % time_limit
}
if normalize:
self.audio_params['RemoveMean'] = 'yes'
self.audio_params['ScaleMax'] = normalize
self.out_attrs = {
'normalize': normalize or '-1',
'version': yaafecore.getYaafeVersion(),
'samplerate': str(sample_rate),
'resample': 'yes' if resample else 'no'
}
self.dataflow = DataFlow()
def get_dataflow(cls, params, samplerate):
nboct = decimal.Decimal(params['CQTNbOctaves'])
original_fs = samplerate
original_step = int(params['stepSize'])
samplerate = decimal.Decimal(samplerate)
minFreq = decimal.Decimal(params['CQTMinFreq']) * (2 ** (nboct - 1))
bandwidth = decimal.Decimal('0.95')
# create dataflow
df = DataFlow()
signal_node = None
# decimate until first octave to analyse
nbdecim = 0
while ((bandwidth * samplerate / 8) > minFreq):
s = df.createNode('Decimate2', {})
if signal_node:
df.link(signal_node, '', s, '')
signal_node = s
samplerate = samplerate / 2
nbdecim = nbdecim + 1
# check stepsize is OK with decimation
# stepSize = int(params['stepSize'])
# if (stepSize % (2**(nbdecim+nboct-1)))!=0:
# stepSize = stepSize - stepSize % (2**(nbdecim+nboct-1))
# print 'WARNING: adjust stepSize to %i to make it compatible with successive decimation'%stepSize
# compute octave CQT parameters
Q = 2 / (2 ** (1.0 / int(params['CQTBinsPerOctave'])) - 1)
fftLen = Q * float(samplerate / minFreq)
fftLen = pow(2, math.ceil(math.log(fftLen) / math.log(2)))
# current_stepSize = stepSize / (2**nbdecim)
oct_params = {'CQTBinsPerOctave': params['CQTBinsPerOctave'],
'CQTAlign': params['CQTAlign'],
'CQTMinFreq': '%s' % str(minFreq / samplerate),
'CQTMaxFreq': '%s' % str(2 * minFreq / samplerate - decimal.Decimal('1e-14'))}
# for each octave, analysis, concatenate and decimation
concat_node = df.createNode('Concatenate', {})
for oct in range(nboct, 0, -1):
frames = df.createNode('AdvancedFrameTokenizer',
{'blockSize': '%i' % fftLen,
'outStepSize': '%i' % original_step,
'outSampleRate': '%f' % original_fs})
if signal_node:
df.link(signal_node, '', frames, '')
cspec = df.createNode(
'FFT', {'FFTLength': '%i' % fftLen, 'FFTWindow': 'None'})
df.link(frames, '', cspec, '')
oct_cq = df.createNode('CQT', oct_params)
df.link(cspec, '', oct_cq, '')
df.link(oct_cq, '', concat_node, '%i' % (oct - 1))
if (oct == 1):
# no more octave to analyze, no need to decimate any more
break
# decimation for next actave analysis
s = df.createNode('Decimate2', {})
if signal_node:
df.link(signal_node, '', s, '')
signal_node = s
# current_stepSize = current_stepSize / 2
minFreq = minFreq / 2
return df
def load(self, dataflow):
"""
Configure engine according to the given dataflow.
:param dataflow: dataflow object or filename of a dataflow file.
:type dataflow: :py:class:`DataFlow` or string
:return: True on success, False on fail.
"""
if type(dataflow) is str:
df = DataFlow()
if df.load(dataflow):
dataflow = df
if type(dataflow) is DataFlow:
return yc.engine_load(self.ptr, dataflow.ptr) and True or False
raise TypeError('dataflow parameter must be a DataFlow object or '
'dataflow filename !')
def addFeature(self, definition):
"""
Add a feature defined according the
:ref:`feature definition syntax <featplan>`.
:param definition: feature definition
:type definition: string
:rtype: True on success, False on fail.
"""
data = definition.split(':')
if not len(data) == 2:
print 'Syntax error in "%s"' % definition
return False
name, featdef = data
dataflow = DataFlow()
dataflow.createInput('audio', self.audio_params)
if featdef.strip():
for s in featdef.split('>'):
s = s.strip()
bb = s.split(' ')
feat = AudioFeatureFactory.get_feature(bb[0])
if not feat:
return False
params = {}
for d in bb[1:]:
if len(d) == 0:
continue
if not '=' in d:
print 'Invalid feature parameter "%s"' % d
return False
dd = d.split('=')
if not len(dd) == 2:
print 'Syntax error in feature parameter "%s"' % d
return False
params[dd[0]] = dd[1]
dataflow.append(feat.get_dataflow(params, self.sample_rate))
fNode = dataflow.finalNodes()[0]
feat_attrs = self.out_attrs.copy()
feat_attrs['yaafedefinition'] = featdef.strip()
outNode = dataflow.createOutput(name, feat_attrs)
dataflow.link(fNode, '', outNode, '')
self.dataflow.merge(dataflow)
return True
def get_dataflow(cls, params, samplerate):
df = DataFlow()
df.createNode('SlopeIntegrator', params)
return df
def get_dataflow(cls, params, samplerate):
df = DataFlow()
df.createNode('Derivate', params)
return df
def get_dataflow(cls, params, samplerate):
df = DataFlow()
dataflow_safe_append(df, 'Cepstrum', params)
return df
def get_dataflow(cls, params, samplerate):
df = DataFlow()
df.createNode('FrameTokenizer', params)
return df
def get_dataflow(cls, params, samplerate):
df = DataFlow()
dataflow_safe_append(df, 'AutoCorrelationPeaksIntegrator', params)
return df
def get_dataflow(cls, params, samplerate):
df = DataFlow()
df.createNode('SimpleThresholdClassification', params)
return df
def get_dataflow(cls, params, samplerate):
df = DataFlow()
df.createNode('AccumulateSameValues', params)
return df
def get_dataflow(cls, params, samplerate):
df = DataFlow()
df.createNode('WindowConvolution', params)
return df
def get_dataflow(cls, params, samplerate):
df = DataFlow()
df.createNode('DilationFilter', params)
return df
