def __init__(self, source,cf,update_interval,param={}):
CombinedFilterbank.__init__(self, source)
source = self.get_modified_source()
parameters=set_parameters(cf,param)
ERBspace = mean(diff(parameters['ERBrate']))
cf=atleast_1d(cf)
#bank of passive gammachirp filters. As the control path uses the same passive filterbank than the signal path (buth shifted in frequency)
#this filterbanl is used by both pathway.
pGc=LogGammachirp(source,cf,b=parameters['b1'], c=parameters['c1'])
fp1 = cf + parameters['c1']*parameters['ERBwidth']*parameters['b1']/parameters['order_gc']
nbr_cf=len(cf)
#### Control Path ####
n_ch_shift = round(parameters['lct_ERB']/ERBspace); #value of the shift in channels
indch1_control = minimum(maximum(1, arange(1,nbr_cf+1)+n_ch_shift),nbr_cf).astype(int)-1
fp1_control = fp1[indch1_control]
pGc_control=RestructureFilterbank(pGc,indexmapping=indch1_control)
fr2_control = parameters['frat_control']*fp1_control
asym_comp_control=AsymmetricCompensation(pGc_control, fr2_control,b=parameters['b2'], c=parameters['c2'])
#### Signal Path ####
fr1=fp1*parameters['frat0']
signal_path= AsymmetricCompensation(pGc, fr1,b=parameters['b2'], c=parameters['c2'])
#### Controler ####
updater = AsymCompUpdate(signal_path,source.samplerate,fp1,parameters) #the updater
control = ControlFilterbank(signal_path, [pGc_control,asym_comp_control], signal_path, updater, update_interval)
self.set_output(control)
def __init__(self, source, cf, type='human', param={}):
CombinedFilterbank.__init__(self, source)
source = self.get_modified_source()
cf = atleast_1d(cf)
nbr_cf=len(cf)
parameters=set_parameters(cf,type,param)
#conversion to stape velocity (which are the units needed for the further centres)
source=source*parameters['stape_scale']
#### Linear Pathway ####
#bandpass filter (second order gammatone filter)
cf_linear=10**(parameters['cf_lin_p0']+parameters['cf_lin_m']*log10(cf))
bandwidth_linear=10**(parameters['bw_lin_p0']+parameters['bw_lin_m']*log10(cf))
gammatone=ApproximateGammatone(source, cf_linear, bandwidth_linear, order=parameters['order_linear'])
#linear gain
g=10**(parameters['g_p0']+parameters['g_m']*log10(cf))
func_gain=lambda x:g*x
gain= FunctionFilterbank(gammatone,func_gain)
#low pass filter(cascade of 4 second order lowpass butterworth filters)
cutoff_frequencies_linear=10**(parameters['lp_lin_cutoff_p0']+parameters['lp_lin_cutoff_m']*log10(cf))
order_lowpass_linear=2
lp_l=LowPass(gain,cutoff_frequencies_linear)
lowpass_linear=Cascade(gain,lp_l,4)
#### Nonlinear Pathway ####
#bandpass filter (third order gammatone filters)
cf_nonlinear=10**(parameters['cf_nl_p0']+parameters['cf_nl_m']*log10(cf))
bandwidth_nonlinear=10**(parameters['bw_nl_p0']+parameters['bw_nl_m']*log10(cf))
bandpass_nonlinear1=ApproximateGammatone(source, cf_nonlinear, bandwidth_nonlinear, order=parameters['order_nonlinear'])
#compression (linear at low level, compress at high level)
a=10**(parameters['a_p0']+parameters['a_m']*log10(cf)) #linear gain
b=10**(parameters['b_p0']+parameters['b_m']*log10(cf))
v=10**(parameters['c_p0']+parameters['c_m']*log10(cf))#compression exponent
func_compression=lambda x:sign(x)*minimum(a*abs(x),b*abs(x)**v)
compression=FunctionFilterbank(bandpass_nonlinear1, func_compression)
#bandpass filter (third order gammatone filters)
bandpass_nonlinear2=ApproximateGammatone(compression, cf_nonlinear, bandwidth_nonlinear, order=parameters['order_nonlinear'])
#low pass filter
cutoff_frequencies_nonlinear=10**(parameters['lp_nl_cutoff_p0']+parameters['lp_nl_cutoff_m']*log10(cf))
order_lowpass_nonlinear=2
lp_nl=LowPass(bandpass_nonlinear2,cutoff_frequencies_nonlinear)
lowpass_nonlinear=Cascade(bandpass_nonlinear2,lp_nl,3)
#adding the two pathways
drnl_filter=lowpass_linear+lowpass_nonlinear
self.set_output(drnl_filter)
def __init__(self, source, cf, update_interval=1, param={}):
CombinedFilterbank.__init__(self, source)
source = self.get_modified_source()
parameters = set_parameters(cf, param)
ERBspace = mean(diff(parameters['ERBrate']))
cf = atleast_1d(cf)
#bank of passive gammachirp filters. As the control path uses the same passive filterbank than the signal path (buth shifted in frequency)
#this filterbanl is used by both pathway.
pGc = LogGammachirp(source, cf, b=parameters['b1'], c=parameters['c1'])
# self.gc.filt_b=pGc.filt_b
# self.gc.filt_a=pGc.filt_a
fp1 = cf + parameters['c1'] * parameters['ERBwidth'] * parameters[
'b1'] / parameters['order_gc']
nbr_cf = len(cf)
#### Control Path ####
n_ch_shift = round(parameters['lct_ERB'] / ERBspace)
#value of the shift in channels
indch1_control = minimum(
maximum(1,
arange(1, nbr_cf + 1) + n_ch_shift),
nbr_cf).astype(int) - 1
fp1_control = fp1[indch1_control]
pGc_control = RestructureFilterbank(pGc, indexmapping=indch1_control)
fr2_control = parameters['frat_control'] * fp1_control
asym_comp_control = AsymmetricCompensation(pGc_control,
fr2_control,
b=parameters['b2'],
c=parameters['c2'])
#### Signal Path ####
fr1 = fp1 * parameters['frat0']
signal_path = AsymmetricCompensation(pGc,
fr1,
b=parameters['b2'],
c=parameters['c2'])
# self.asym_comp.filt_b=signal_path.filt_b
# self.asym_comp.filt_a=signal_path.filt_a
#### Controler ####
updater = AsymCompUpdate(signal_path, source.samplerate, fp1,
parameters) #the updater
control = ControlFilterbank(signal_path,
[pGc_control, asym_comp_control],
signal_path, updater, update_interval)
self.set_output(control)
def __init__(self, source,cf,update_interval,param={}):
CombinedFilterbank.__init__(self, source)
source = self.get_modified_source()
cf = atleast_1d(cf)
nbr_cf=len(cf)
parameters=set_parameters(cf,param)
if int(source.samplerate)!=50000:
warnings.warn('To use the PMFR cochlear model the sample rate should be 50kHz')
if not have_scikits_samplerate:
raise ImportError('To use the PMFR cochlear model the sample rate should be 50kHz and scikits.samplerate package is needed for resampling')
#source=source.resample(50*kHz)
warnings.warn('The input to the PMFR cochlear model has been resampled to 50kHz')
samplerate=source.samplerate
##### Control Path ####
# wide band pass control
#Initialisation of the filter coefficients of the bandpass filter
x_cf=11.9*log10(0.8+cf/456)
f_shift=(10**((x_cf+1.2)/11.9)-0.8)*456-cf
filt_b=zeros((len(cf), 3, 3))
filt_a=zeros((len(cf), 3, 3))
poles=zeros((len(cf),3),dtype='complex')
wbw=cf/4
poles[:,0:3]=tile(-2*pi*wbw+1j*2*pi*(cf+f_shift),[3,1]).T
poles=(1+poles/(2*samplerate))/(1-poles/(2*samplerate)) #bilinear transform
bfp=2*pi*cf
zz=exp(1j*bfp/samplerate)
gain_norm=abs((zz**2+2*zz+1)/(zz**2-zz*(poles[:,0]+conj(poles[:,0]))+poles[:,0]*conj(poles[:,0])))**3*2
filt_b[:,:,0]=vstack([ones(len(cf)),2*ones(len(cf)),ones(len(cf))]).T
filt_b[:,:,1]=vstack([ones(len(cf)),2*ones(len(cf)),ones(len(cf))]).T
filt_b[:,:,2]=vstack([ones(len(cf)),zeros(len(cf)),-ones(len(cf))]).T
for iorder in xrange(3):
filt_a[:,:,iorder]=vstack([ones(len(cf)),real(-(squeeze(poles[:,iorder])+conj(squeeze(poles[:,iorder])))),real(squeeze(poles[:,iorder])*conj(squeeze(poles[:,iorder])))]).T
filt_b[:,:,2]=filt_b[:,:,2]/tile(gain_norm,[3,1]).T
BP_control= LinearFilterbank(source,filt_b,filt_a) #bandpass filter instantiation (a controller will vary its coefficients)
# first non linearity of control path
Acp,Bcp,Ccp=100.,2.5,0.60
func_NL1_control=lambda x:sign(x)*Bcp*log(1.+Acp*abs(x)**Ccp)
NL1_control=FunctionFilterbank(BP_control,func_NL1_control)
# second non linearity of control path
asym,s0,x1,s1=7.,8.,5.,3.
shift = 1./(1.+asym)
x0 = s0*log((1.0/shift-1)/(1+exp(x1/s1)))
func_NL2_control=lambda x:(1.0/(1.0+exp(-(x-x0)/s0)*(1.0+exp(-(x-x1)/s1)))-shift)*parameters['nlgain']
NL2_control=FunctionFilterbank(NL1_control,func_NL2_control)
#control low pass filter (its output will be used to control the signal path)
LP_control=Butterworth(NL2_control, nbr_cf, 3, parameters['fc_LP_control'], btype='low')
#low pass filter for feedback to control band pass (its output will be used to control the control path)
LP_feed_back= Butterworth(LP_control, nbr_cf, 3,parameters['fc_LP_fb'], btype='low')
#### Signal Path ####
#Initisialisation of the filter coefficients of the bandpass filter
x_cf=11.9*log10(0.8+cf/456)
N=len(cf)
f_shift=(10**((x_cf+1.2)/11.9)-0.8)*456-cf
filt_b=zeros((len(cf), 3, 10))
filt_a=zeros((len(cf), 3, 10))
poles=zeros((len(cf),10),dtype='complex')
a=-parameters['rgain']+1j*parameters['fp1']*2*pi
poles[:,0:4]=tile(-parameters['rgain']+1j*parameters['fp1']*2*pi,[4,1]).T
poles[:,4:8]=tile(real(poles[:,0])- parameters['ta']+1j*(imag(poles[:,0])- parameters['tb']),[4,1]).T
poles[:,8:10]=tile((real(poles[:,0])+real(poles[:,4]))*.5+1j*(imag(poles[:,0])+imag(poles[:,4]))*.5,[2,1]).T
zero_r=parameters['zero_r']
poles=(1+poles/(2*samplerate))/(1-poles/(2*samplerate))
zero_r=(1+zero_r/(2*samplerate))/(1-zero_r/(2*samplerate))
bfp=2*pi*cf
gain_norm=1
zz=exp(1j*bfp/samplerate)
for ii in xrange(10):
gain_norm=gain_norm*abs((zz**2-zz*(-1+zero_r)-zero_r)/(zz**2-zz*(poles[:,ii]+conj(poles[:,ii]))+poles[:,ii]*conj(poles[:,ii])))
for iorder in xrange(10):
filt_b[:,:,iorder]=vstack([ones(len(cf)),-(-1+zero_r),-zero_r]).T
filt_a[:,:,iorder]=vstack([ones(N),real(-(squeeze(poles[:,iorder])+conj(squeeze(poles[:,iorder])))),real(squeeze(poles[:,iorder])*conj(squeeze(poles[:,iorder])))]).T
filt_b[:,:,9]=filt_b[:,:,9]/tile(gain_norm,[3,1]).T
signal_path= LinearFilterbank(source,filt_b,filt_a) #bandpass filter instantiation (a controller will vary its coefficients)
#controlers definition
updater_control_path=BP_control_update(BP_control,samplerate,cf,param) #instantiation of the updater for the control path
control1 = ControlFilterbank(signal_path, LP_feed_back, BP_control,updater_control_path, update_interval) #controler for the band pass filter of the control path
updater_signal_path=BP_signal_update(signal_path,samplerate,cf,param) #instantiation of the updater for the signal path
control2 = ControlFilterbank(control1, LP_control, signal_path,updater_signal_path, update_interval) #controler for the band pass filter of the signal path
self.set_output(control2)
def __init__(self, source, cf, update_interval, param={}):
file = "/home/bertrand/Data/MatlabProg/brian_hears/ZilanyCarney-JASAcode-2009/wbout.mat"
X = loadmat(file, struct_as_record=False)
wbout = Sound(X['wbout'].flatten())
wbout.samplerate = 100 * kHz
CombinedFilterbank.__init__(self, source)
# source = self.get_modified_source()
cf = atleast_1d(cf)
nbr_cf = len(cf)
samplerate = source.samplerate
parameters = set_parameters(cf, param)
# if int(source.samplerate)!=50000:
# warnings.warn('To use the PMFR cochlear model the sample rate should be 50kHz')
# if not have_scikits_samplerate:
# raise ImportError('To use the PMFR cochlear model the sample rate should be 50kHz and scikits.samplerate package is needed for resampling')
# #source=source.resample(50*kHz)
# warnings.warn('The input to the PMFR cochlear model has been resampled to 50kHz')
cohc = 1 # ohc scaling factor: 1 is normal OHC function; 0 is complete OHC dysfunction
cihc = 1 # i ihc scaling factor: 1 is normal IHC function; 0 is complete IHC dysfunction
bmplace = 11.9 * log10(0.80 + cf / 456.0)
centerfreq = 456.0 * (pow(10, (bmplace + 1.2) / 11.9) - 0.80)
CAgain = minimum(
60,
maximum(15 * ones(len(cf)),
52 / 2 * (tanh(2.2 * log10(cf / 600) + 0.15) + 1)))
# Parameters for the control-path wideband filter =======*/
bmorder = 3
Taumax, Taumin = get_tauwb(cf, CAgain, bmorder)
taubm = cohc * (Taumax - Taumin) + Taumin
ratiowb = Taumin / Taumax
#====== Parameters for the signal-path C1 filter ======*/
bmTaumax, bmTaumin, ratiobm = get_taubm(cf, CAgain, Taumax)
bmTaubm = cohc * (bmTaumax - bmTaumin) + bmTaumin
fcohc = bmTaumax / bmTaubm
# Parameters for the control-path wideband filter =======*/
wborder = 3
TauWBMax = Taumin + 0.2 * (Taumax - Taumin)
TauWBMin = TauWBMax / Taumax * Taumin
tauwb = TauWBMax + (bmTaubm - bmTaumax) * (TauWBMax - TauWBMin) / (
bmTaumax - bmTaumin)
[wbgain, grdelay] = gain_groupdelay(1. / self.samplerate, centerfreq,
cf, tauwb)
# Nonlinear asymmetry of OHC function and IHC C1 transduction function*/
ohcasym = 7.0
ihcasym = 3.0
# ##### Control Path ####
# print tauwb*pi*centerfreq
# gt_control = BiQuadratic(source, centerfreq,tauwb*2*pi*centerfreq)
gt_control = ApproximateGammatone(source,
centerfreq,
1. / (2 * pi * tauwb),
order=3)
# gt_control = LinearGammachirp(source, centerfreq,tauwb, c=0)
# gt_control = Gammatone(source, centerfreq, b=1./(pi*tauwb))
# def wb_gain(x,cf=array([1000]),tauwb=array([.0003]),TauWBMax=array([.0003]),wborder=3):
# print x.shape,cf.shape
# out=zeros_like(x)
# for ix in xrange(len(x)):
# out[ix,:] = (tauwb/TauWBMax)**wborder*x[ix,:]*10e3*maximum(ones(len(cf)),cf/5e3)
# return out
# gt_control1 = FunctionFilterbank(gt_control,wb_gain,cf=cf,tauwb=tauwb,TauWBMax=TauWBMax,wborder=wborder)
max_temp = (tauwb / TauWBMax)**wborder * 10e3 * maximum(
ones(len(cf)), cf / 5e3)
gt_control1 = FunctionFilterbank(gt_control, lambda x: x * max_temp)
# first non linearity of control path
NL1_control = FunctionFilterbank(gt_control1,
boltzman,
asym=ohcasym,
s0=12.0,
s1=5.0,
x1=5.0)
# #control low pass filter (its output will be used to control the signal path)
LP_control = LowPass_filter(NL1_control, cf, 600, 1.0, 2)
# # second non linearity of control path
NL2_control = FunctionFilterbank(LP_control,
OHC_transduction,
taumin=bmTaumin,
taumax=bmTaumax,
asym=ohcasym)
#
# #### C1 ####
rsigma = zeros(len(cf))
c1_coefficients = Chirp_Coefficients(cf, bmTaumax, samplerate, rsigma,
1 * ones(len(cf)))
[filt_b, filt_a] = c1_coefficients.return_coefficients()
C1_filter = LinearFilterbank(source, filt_b, filt_a)
C1_IHC = FunctionFilterbank(C1_filter,
IHC_transduction,
slope=0.1,
asym=ihcasym,
sign=1)
#### C2 ####
c2_coefficients = Chirp_Coefficients(cf, bmTaumax, samplerate,
0 * ones(len(cf)), 1. / ratiobm)
[filt_b, filt_a] = c2_coefficients.return_coefficients()
C2_filter = LinearFilterbank(source, filt_b, filt_a)
gain_temp = cf**2 / 2e4
C2_IHC_pre = FunctionFilterbank(C2_filter,
lambda x: x * abs(x) * gain_temp)
C2_IHC = FunctionFilterbank(C2_IHC_pre,
IHC_transduction,
slope=0.2,
asym=1.0,
sign=-1)
#
C_IHC = C1_IHC + C2_IHC
#
C_IHC_lp = LowPass_filter(C_IHC, cf, 3000, 1.0, 7)
#controlers definition
updater = Filter_Update(
[C1_filter], c1_coefficients, samplerate, cf, bmTaumax, bmTaumin,
cohc, TauWBMax,
TauWBMin) #instantiation of the updater for the control path
output = ControlFilterbank(
C1_filter, NL2_control, [C1_filter], updater, update_interval
) #controler for the band pass filter of the control path
self.set_output(output)
def __init__(self, source,cf,update_interval,param={}):
file="/home/bertrand/Data/MatlabProg/brian_hears/ZilanyCarney-JASAcode-2009/wbout.mat"
X=loadmat(file,struct_as_record=False)
wbout = Sound(X['wbout'].flatten())
wbout.samplerate = 100*kHz
CombinedFilterbank.__init__(self, source)
# source = self.get_modified_source()
cf = atleast_1d(cf)
nbr_cf=len(cf)
samplerate=source.samplerate
parameters=set_parameters(cf,param)
# if int(source.samplerate)!=50000:
# warnings.warn('To use the PMFR cochlear model the sample rate should be 50kHz')
# if not have_scikits_samplerate:
# raise ImportError('To use the PMFR cochlear model the sample rate should be 50kHz and scikits.samplerate package is needed for resampling')
# #source=source.resample(50*kHz)
# warnings.warn('The input to the PMFR cochlear model has been resampled to 50kHz')
cohc =1 # ohc scaling factor: 1 is normal OHC function; 0 is complete OHC dysfunction
cihc = 1 # i ihc scaling factor: 1 is normal IHC function; 0 is complete IHC dysfunction
bmplace = 11.9 * log10(0.80 + cf / 456.0)
centerfreq = 456.0*(pow(10,(bmplace+1.2)/11.9)-0.80)
CAgain = minimum(60,maximum(15*ones(len(cf)),52/2*(tanh(2.2*log10(cf/600)+0.15)+1)))
# Parameters for the control-path wideband filter =======*/
bmorder = 3;
Taumax,Taumin = get_tauwb(cf,CAgain,bmorder)
taubm = cohc*(Taumax-Taumin)+Taumin;
ratiowb = Taumin/Taumax;
#====== Parameters for the signal-path C1 filter ======*/
bmTaumax,bmTaumin,ratiobm = get_taubm(cf,CAgain,Taumax)
bmTaubm = cohc*(bmTaumax-bmTaumin)+bmTaumin;
fcohc = bmTaumax/bmTaubm;
# Parameters for the control-path wideband filter =======*/
wborder = 3
TauWBMax = Taumin+0.2*(Taumax-Taumin);
TauWBMin = TauWBMax/Taumax*Taumin;
tauwb = TauWBMax+(bmTaubm-bmTaumax)*(TauWBMax-TauWBMin)/(bmTaumax-bmTaumin);
[wbgain,grdelay] = gain_groupdelay(1./self.samplerate,centerfreq,cf,tauwb)
# Nonlinear asymmetry of OHC function and IHC C1 transduction function*/
ohcasym = 7.0
ihcasym = 3.0
# ##### Control Path ####
# print tauwb*pi*centerfreq
# gt_control = BiQuadratic(source, centerfreq,tauwb*2*pi*centerfreq)
gt_control = ApproximateGammatone(source, centerfreq, 1./(2*pi*tauwb), order=3)
# gt_control = LinearGammachirp(source, centerfreq,tauwb, c=0)
# gt_control = Gammatone(source, centerfreq, b=1./(pi*tauwb))
# def wb_gain(x,cf=array([1000]),tauwb=array([.0003]),TauWBMax=array([.0003]),wborder=3):
# print x.shape,cf.shape
# out=zeros_like(x)
# for ix in xrange(len(x)):
# out[ix,:] = (tauwb/TauWBMax)**wborder*x[ix,:]*10e3*maximum(ones(len(cf)),cf/5e3)
# return out
# gt_control1 = FunctionFilterbank(gt_control,wb_gain,cf=cf,tauwb=tauwb,TauWBMax=TauWBMax,wborder=wborder)
max_temp = (tauwb/TauWBMax)**wborder*10e3*maximum(ones(len(cf)),cf/5e3)
gt_control1 = FunctionFilterbank(gt_control,lambda x:x*max_temp)
# first non linearity of control path
NL1_control=FunctionFilterbank(gt_control1,boltzman,asym=ohcasym,s0=12.0,s1=5.0,x1=5.0)
# #control low pass filter (its output will be used to control the signal path)
LP_control = LowPass_filter(NL1_control,cf,600,1.0,2)
# # second non linearity of control path
NL2_control=FunctionFilterbank(LP_control,OHC_transduction,taumin=bmTaumin, taumax=bmTaumax, asym=ohcasym)
#
# #### C1 ####
rsigma = zeros(len(cf))
c1_coefficients = Chirp_Coefficients(cf, bmTaumax, samplerate, rsigma,1*ones(len(cf)))
[filt_b,filt_a] = c1_coefficients.return_coefficients()
C1_filter = LinearFilterbank(source,filt_b,filt_a)
C1_IHC = FunctionFilterbank(C1_filter,IHC_transduction,slope = 0.1,asym = ihcasym,sign=1)
#### C2 ####
c2_coefficients = Chirp_Coefficients(cf, bmTaumax, samplerate, 0*ones(len(cf)),1./ratiobm)
[filt_b,filt_a] = c2_coefficients.return_coefficients()
C2_filter = LinearFilterbank(source,filt_b,filt_a)
gain_temp = cf**2/2e4
C2_IHC_pre = FunctionFilterbank(C2_filter,lambda x:x*abs(x)*gain_temp)
C2_IHC = FunctionFilterbank(C2_IHC_pre,IHC_transduction,slope = 0.2,asym = 1.0,sign=-1)
#
C_IHC = C1_IHC + C2_IHC
#
C_IHC_lp = LowPass_filter(C_IHC,cf,3000,1.0,7)
#controlers definition
updater=Filter_Update([C1_filter],c1_coefficients,samplerate,cf,bmTaumax,bmTaumin,cohc,TauWBMax,TauWBMin) #instantiation of the updater for the control path
output = ControlFilterbank(C1_filter, NL2_control, [C1_filter],updater, update_interval) #controler for the band pass filter of the control path
self.set_output(output)
