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 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 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(int(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 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()
df.createNode('FrameTokenizer', 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('SlopeIntegrator', params)
return df
def get_dataflow(cls, params, samplerate):
df = DataFlow()
df.createNode("DilationFilter", params)
return df
def get_dataflow(cls, params, samplerate):
df = DataFlow()
df.createNode("SimpleNoiseGate", 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
def get_dataflow(cls, params, samplerate):
df = DataFlow()
df.createNode('FrameTokenizer', 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()
df.createNode("WindowConvolution", 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("HistogramIntegrator", params)
return df
