def _gammatonefilter(self, signal, chan=0):
""" a gammatone filter bank """
B, A = filtering.gammatone_biquad(44100, self.size, 150)
# run the 4 biquads
y = scipy.signal.lfilter(B[chan, 0, :], A[chan, 0, :], signal)
y = scipy.signal.lfilter(B[chan, 1, :], A[chan, 1, :], y)
y = scipy.signal.lfilter(B[chan, 2, :], A[chan, 2, :], y)
y = scipy.signal.lfilter(B[chan, 3, :], A[chan, 3, :], y)
return y
def _gammatonefilter(self, signal, chan=0): """ a gammatone filter bank """ B,A = filtering.gammatone_biquad(44100,self.size,150) # run the 4 biquads y = scipy.signal.lfilter(B[chan,0,:],A[chan,0,:],signal) y = scipy.signal.lfilter(B[chan,1,:],A[chan,1,:],y) y = scipy.signal.lfilter(B[chan,2,:],A[chan,2,:],y) y = scipy.signal.lfilter(B[chan,3,:],A[chan,3,:],y) return y
def testSerialIIRFilters(self, level=1):
""" test correspondence of a cascaded IIR filter """
# set parameters for non-interacting neurons:
# new state only depends on the input
self.net.setSimAlgorithm(SIM_FILTER)
self.ins = 1
self.outs = 1
self.net.setReservoirAct(ACT_LINEAR)
self.net.setOutputAct(ACT_LINEAR)
self.net.setInputs(self.ins)
self.net.setOutputs(self.outs)
self.net.setInitParam(ALPHA, 0.)
self.net.setInitParam(FB_CONNECTIVITY, 0.)
self.net.setInitParam(IN_CONNECTIVITY, 1.)
self.net.setInitParam(IN_SCALE, 0.)
self.net.setInitParam(IN_SHIFT, 1.)
self.net.init()
# set paramas for a gammatone filter
# (cascade of 4 biquads)
Btmp, Atmp = filtering.gammatone_biquad(44100, self.size, 150)
B = N.empty((self.size, 12))
A = N.empty((self.size, 12))
serial = 4
B[:, 0:3] = Btmp[:, 0, :]
B[:, 3:6] = Btmp[:, 1, :]
B[:, 6:9] = Btmp[:, 2, :]
B[:, 9:12] = Btmp[:, 3, :]
A[:, 0:3] = Atmp[:, 0, :]
A[:, 3:6] = Atmp[:, 1, :]
A[:, 6:9] = Atmp[:, 2, :]
A[:, 9:12] = Atmp[:, 3, :]
self.net.setIIRCoeff(B, A, serial)
# simulate network step by step:
# the states x are now only the filtered input signal, which
# is the same for each neuron ! (because ALPHA = 0)
indata = N.random.rand(1, self.sim_size) * 2 - 1
indata = N.asfarray(indata, self.dtype)
filterout = N.zeros((1, self.sim_size), self.dtype)
outtmp = N.zeros((self.outs), self.dtype)
for n in range(self.sim_size):
intmp = indata[:, n].copy()
self.net.simulateStep(intmp, outtmp)
filterout[0, n] = self.net.getX()[0]
# now calculate the same with scipy.signal.lfilter
filterout2 = self._gammatonefilter(indata.flatten())
assert_array_almost_equal(filterout.flatten(), filterout2)
def testSerialIIRFilters(self, level=1):
""" test correspondence of a cascaded IIR filter """
# set parameters for non-interacting neurons:
# new state only depends on the input
self.net.setSimAlgorithm(SIM_FILTER)
self.ins = 1
self.outs = 1
self.net.setReservoirAct(ACT_LINEAR)
self.net.setOutputAct(ACT_LINEAR)
self.net.setInputs( self.ins )
self.net.setOutputs( self.outs )
self.net.setInitParam(ALPHA, 0.)
self.net.setInitParam(FB_CONNECTIVITY, 0.)
self.net.setInitParam(IN_CONNECTIVITY, 1.)
self.net.setInitParam(IN_SCALE, 0.)
self.net.setInitParam(IN_SHIFT, 1.)
self.net.init()
# set paramas for a gammatone filter
# (cascade of 4 biquads)
Btmp,Atmp = filtering.gammatone_biquad(44100,self.size,150)
B = N.empty((self.size,12))
A = N.empty((self.size,12))
serial = 4
B[:,0:3] = Btmp[:,0,:]
B[:,3:6] = Btmp[:,1,:]
B[:,6:9] = Btmp[:,2,:]
B[:,9:12] = Btmp[:,3,:]
A[:,0:3] = Atmp[:,0,:]
A[:,3:6] = Atmp[:,1,:]
A[:,6:9] = Atmp[:,2,:]
A[:,9:12] = Atmp[:,3,:]
self.net.setIIRCoeff(B,A,serial)
# simulate network step by step:
# the states x are now only the filtered input signal, which
# is the same for each neuron ! (because ALPHA = 0)
indata = N.random.rand(1,self.sim_size)*2-1
indata = N.asfarray(indata, self.dtype)
filterout = N.zeros((1,self.sim_size),self.dtype)
outtmp = N.zeros((self.outs),self.dtype)
for n in range(self.sim_size):
intmp = indata[:,n].copy()
self.net.simulateStep( intmp, outtmp )
filterout[0,n] = self.net.getX()[0]
# now calculate the same with scipy.signal.lfilter
filterout2 = self._gammatonefilter( indata.flatten() )
assert_array_almost_equal(filterout.flatten(),filterout2)
def setGammatoneFilters(self, fs, lowFreq):
""" Set to each neuron one gammatone filter so that the filter
frequencies will reach from lowFreq to fs/2 (each neuron will
have a different gammatone filter !)
fs sample rate
lowFreq lowest filter frequency
"""
size = self.getSize()
B, A = filtering.gammatone_biquad(fs, size, lowFreq)
# 4 biquads
self.B = N.empty((size, 12))
self.A = N.empty((size, 12))
self.serial = 4
self.B[:, 0:3] = B[:, 0, :]
self.B[:, 3:6] = B[:, 1, :]
self.B[:, 6:9] = B[:, 2, :]
self.B[:, 9:12] = B[:, 3, :]
self.A[:, 0:3] = A[:, 0, :]
self.A[:, 3:6] = A[:, 1, :]
self.A[:, 6:9] = A[:, 2, :]
self.A[:, 9:12] = A[:, 3, :]
def setGammatoneFilters(self, fs, lowFreq):
""" Set to each neuron one gammatone filter so that the filter
frequencies will reach from lowFreq to fs/2 (each neuron will
have a different gammatone filter !)
fs sample rate
lowFreq lowest filter frequency
"""
size = self.getSize()
B, A = filtering.gammatone_biquad(fs, size, lowFreq)
# 4 biquads
self.B = N.empty((size, 12))
self.A = N.empty((size, 12))
self.serial = 4
self.B[:, 0:3] = B[:, 0, :]
self.B[:, 3:6] = B[:, 1, :]
self.B[:, 6:9] = B[:, 2, :]
self.B[:, 9:12] = B[:, 3, :]
self.A[:, 0:3] = A[:, 0, :]
self.A[:, 3:6] = A[:, 1, :]
self.A[:, 6:9] = A[:, 2, :]
self.A[:, 9:12] = A[:, 3, :]
