def differentdeconcolution():
sampling_rate = 48000.0
start_freq = 100.0
stop_freq = 20000.0
length = 2**18
fade_out = 0.00
fade_in = 0.00
branches = 5
sine = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate, length=length, start_frequency=start_freq,
stop_frequency=stop_freq, fade_out= fade_out,fade_in=fade_in)
sine_f = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate, length=length, start_frequency=start_freq,
stop_frequency=stop_freq, fade_out= 0.02,fade_in=0.02)
source_dir = "C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_db/"
load_sine = sumpf.modules.SignalFile(filename=os.path.join(source_dir,"sine.npz"), format=sumpf.modules.SignalFile.WAV_FLOAT)
load_sine_f = sumpf.modules.SignalFile(filename=os.path.join(source_dir,"sine_f.npz"), format=sumpf.modules.SignalFile.WAV_FLOAT)
output_sine = sumpf.modules.SplitSignal(data=load_sine.GetSignal(),channels=[1]).GetOutput()
output_sine_f = sumpf.modules.SplitSignal(data=load_sine_f.GetSignal(),channels=[1]).GetOutput()
ir_reverse = nlsp.get_nl_impulse_response(sine,output_sine)
ir_specinv = nlsp.get_impulse_response(sine.GetOutput(),output_sine,start_freq,stop_freq)
ir_specinv_fade = nlsp.get_impulse_response(sine_f.GetOutput(),output_sine_f,start_freq,stop_freq)
plot.relabelandplot(ir_specinv,show=True,label="IR_SpectralInversion")
plot.relabelandplot(ir_specinv_fade,show=True,label="IR_SpectralInversion_fade")
plot.relabelandplot(ir_reverse,show=True,label="IR_Reverse")
def clippingHGMevaluation(input_generator, branches, Plot, reference=None):
for t in range(8, 11):
t = t / 10.0
thresholds = [-t, t]
input_signal = input_generator.GetOutput()
nl_functions = [
nlsp.function_factory.hardclip(thresholds),
] * branches
filter_spec_tofind = nlsp.log_bpfilter(branches=branches,
input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(),
length=len(input_signal))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs,
filter_length)
ref_nlsystem.SetInput(input_signal)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
# sine = sumpf.modules.SineWaveGenerator(frequency=5000.0,phase=0.0,samplingrate=input_signal.GetSamplingRate(),length=len(input_signal)).GetSignal()
sine = sumpf.modules.SweepGenerator(
samplingrate=input_signal.GetSamplingRate(),
length=len(input_signal)).GetSignal()
ref_nlsystem.SetInput(sine)
iden_nlsystem.SetInput(sine)
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplot(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference System",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified System",
show=False)
print "SNR between Reference and Identified output for symmetric hardclipping HGM(thresholds:%r): %r" % (
thresholds,
nlsp.snr(ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput()))
def comparereversedandsi():
sampling_rate = 48000
length = 2**16
branches = 5
excitation = nlsp.NovakSweepGenerator_Sine(start_frequency=20.0,stop_frequency=20000.0,
length=length,sampling_rate=sampling_rate)
input_signal = excitation.GetOutput()
input_spec = sumpf.modules.FourierTransform(input_signal).GetSpectrum()
prp = sumpf.modules.ChannelDataProperties()
prp.SetSignal(input_signal)
filter_spec_tofind = sumpf.modules.FilterGenerator(filterfunction=sumpf.modules.FilterGenerator.BUTTERWORTH(order=100),
frequency=20.0,transform=True,resolution=prp.GetResolution(),
length=prp.GetSpectrumLength()).GetSpectrum()
filter_spec_tofind = [sumpf.modules.InverseFourierTransform(filter_spec_tofind).GetSignal(),]*branches
ref_nlsystem = nlsp.HammersteinGroupModel_up(input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(nlsp.function_factory.power_series,branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1,branches+1))
nlsp.plotstft(ref_nlsystem.GetOutput())
nlsp.plotstft(input_signal)
response = ref_nlsystem.GetOutput()
response_spec = sumpf.modules.FourierTransform(response).GetSpectrum()
reg_inv = sumpf.modules.RegularizedSpectrumInversion(spectrum=input_spec,start_frequency=20.0,stop_frequency=6000.0,epsilon_max=0.1).GetOutput()
reg_specdivision = sumpf.modules.MultiplySpectrums(spectrum1=reg_inv, spectrum2=response_spec)
reg_tf = reg_specdivision.GetOutput()
nl_ir_si = sumpf.modules.InverseFourierTransform(spectrum=reg_tf).GetSignal()
rev = excitation.GetReversedOutput()
rev_spec = sumpf.modules.FourierTransform(rev).GetSpectrum()
out_spec = response_spec / response.GetSamplingRate()
tf = rev_spec * out_spec
ir_sweep = sumpf.modules.InverseFourierTransform(tf).GetSignal()
plot.relabelandplot(nl_ir_si,"spectralinversion_ir",show=False)
plot.relabelandplot(ir_sweep,"reverse_ir",show=True)
def __init__(self, sampling_rate=48000.0, length=2**16, start_frequency=20.0,
stop_frequency=20000.0, max_value=1.0, min_value=-1.0):
self.__max_value = max_value
self.__min_value = min_value
self.__sweep = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate, length=length, start_frequency=start_frequency,
stop_frequency=stop_frequency, fade_out=0.0, fade_in=0.0)
self.__signal = self.__sweep.GetOutput()
def hardvssoft():
sampling_rate = 48000.0
start_freq = 20.0
stop_freq = 20000.0
length = 2**16
fade_out = 0.00
fade_in = 0.00
sine = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate, length=length, start_frequency=start_freq,
stop_frequency=stop_freq, fade_out= fade_out,fade_in=fade_in)
thresholds = [-1.0,1.0]
power = 1.0/3.0
ref_nlsystem_hard_symmetric = sumpf.modules.ClipSignal(thresholds=[-0.7,0.7])
ref_nlsystem_hard_nonsymmetric = sumpf.modules.ClipSignal(thresholds=[-0.6,0.8])
ref_nlsystem_soft = nlsp.NLClipSignal(power=power)
ref_nlsystem_hard_nonsymmetric.SetInput(sine.GetOutput())
ref_nlsystem_hard_symmetric.SetInput(sine.GetOutput())
ref_nlsystem_soft.SetInput(sine.GetOutput())
soft_ir = nlsp.getnl_ir(sine,ref_nlsystem_soft.GetOutput())
hard_symm_ir = nlsp.getnl_ir(sine,ref_nlsystem_hard_symmetric.GetOutput())
hard_nonsymm_ir = nlsp.getnl_ir(sine,ref_nlsystem_hard_nonsymmetric.GetOutput())
# plot.relabelandplot(soft_ir,"soft clipping IR",show=False)
# plot.relabelandplot(hard_nonsymm_ir,"nonsymmetric hard clipping IR",show=False)
# plot.relabelandplot(hard_symm_ir,"symmetric hard clipping IR",show=True)
sine = sumpf.modules.SineWaveGenerator(frequency=1000.0,samplingrate=sampling_rate,length=length)
ref_nlsystem_hard_nonsymmetric.SetInput(sine.GetSignal())
ref_nlsystem_hard_symmetric.SetInput(sine.GetSignal())
ref_nlsystem_soft.SetInput(sine.GetSignal())
# plot.relabelandplot(sumpf.modules.FourierTransform(ref_nlsystem_soft.GetOutput()).GetSpectrum(),"soft clipping output",show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(ref_nlsystem_hard_nonsymmetric.GetOutput()).GetSpectrum(),"nonsymmetric hard clipping output",show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(ref_nlsystem_hard_symmetric.GetOutput()).GetSpectrum(),"symmetric hard clipping output",show=True)
def differentlength_evaluation(input_generator,
branches,
Plot,
reference=None):
length_ref = [2**15, 2**16, 2**17]
length_iden = [2**15, 2**16, 2**17]
input_generator_ref = input_generator
input_generator_iden = input_generator
for signal_length, ref_length in zip(length_iden, length_ref):
input_generator_ref.SetLength(ref_length)
input_ref = input_generator_ref.GetOutput()
filter_spec_tofind = nlsp.log_weightingfilter(branches=branches,
input=input_ref)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_ref,
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_ref.GetSamplingRate(), length=len(input_ref))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs,
filter_length)
ref_nlsystem.SetInput(input_ref)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator_ref,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
input_generator_iden.SetLength(signal_length)
input_iden = input_generator_iden.GetOutput()
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_iden,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_ref))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplotphase(
sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output", False)
plot.relabelandplotphase(
sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output", True)
print "SNR between Reference(length:%r) and Identified output(length:%r) : %r" % (
len(input_ref), len(input_iden),
nlsp.snr(ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput()))
def robustness_excitation_evaluation(input_generator,
branches,
Plot,
reference=None):
excitation_signal_amp = [0.5, 1.0]
sample_signal_amp = [0.5, 1.0, 2.0]
input = input_generator.GetOutput()
for excitation_amp, sample_amp in itertools.product(
excitation_signal_amp, sample_signal_amp):
input_signal = sumpf.modules.AmplifySignal(
input=input, factor=excitation_amp).GetOutput()
sample_signal = nlsp.WhiteGaussianGenerator(
sampling_rate=input_signal.GetSamplingRate(),
length=len(input_signal),
distribution=sumpf.modules.NoiseGenerator.UniformDistribution(
minimum=-sample_amp, maximum=sample_amp))
sample_signal = sample_signal.GetOutput()
filter_spec_tofind = nlsp.log_weightingfilter(branches=branches,
input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(),
length=len(input_signal))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs,
filter_length)
ref_nlsystem.SetInput(input_signal)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=sample_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
ref_nlsystem.SetInput(sample_signal)
if Plot is True:
nlsp.relabelandplotphase(
sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output Scaled", False)
nlsp.relabelandplot(
sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output", True)
print "SNR between Scaled Identified with(amp:%r) and Tested with(amp:%r) output: %r" % (
excitation_amp, sample_amp,
nlsp.snr(ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput()))
def plot_spectrogram():
sampling_rate = 48000
length = 2**18
branches = 5
excitation = nlsp.NovakSweepGenerator_Sine(start_frequency=20.0,stop_frequency=20000.0,
length=length,sampling_rate=sampling_rate)
# plot.plot_spectrogram(excitation.GetReversedOutput())
# plot.plot_spectrogram(excitation.GetOutput())
nlsp.plotstft(excitation.GetOutput())
def hgmallpass_evaluation(input_generator,
branches,
nlfunction,
Plot,
reference=None):
input_signal = input_generator.GetOutput()
allpass = sumpf.modules.ImpulseGenerator(
samplingrate=input_signal.GetSamplingRate(),
length=len(input_signal)).GetSignal()
filter_spec_tofind = [
allpass,
] * branches
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(nlfunction, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(), length=len(input_signal))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs, filter_length)
ref_nlsystem.SetInput(input_signal)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplot(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference System",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified System",
show=True)
print "SNR between Reference and Identified output with all pass filters: %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput())
def harmonics_evaluation():
degree = 5
length = 2**18
input_signal = nlsp.NovakSweepGenerator_Sine(
sampling_rate=sampling_rate,
length=length,
start_frequency=sweep_start_freq,
stop_frequency=sweep_stop_freq)
input_sweep_signal = input_signal.GetOutput()
for i in range(degree, degree + 1):
prp = sumpf.modules.ChannelDataProperties()
prp.SetSignal(input_sweep_signal)
filter = sumpf.modules.FilterGenerator(
filterfunction=sumpf.modules.FilterGenerator.BUTTERWORTH(
order=100),
frequency=24000.0,
transform=False,
resolution=prp.GetResolution(),
length=prp.GetSpectrumLength()).GetSpectrum()
filter_ir = sumpf.modules.InverseFourierTransform(filter).GetSignal()
branch_simple = nlsp.HammersteinModel(
input_signal=input_sweep_signal,
nonlin_func=nlsp.function_factory.power_series(i),
filter_impulseresponse=filter_ir)
branch_up = nlsp.AliasCompensatingHammersteinModelUpandDown(
input_signal=input_sweep_signal,
nonlin_func=nlsp.function_factory.power_series(i),
max_harm=i,
filter_impulseresponse=filter_ir)
branch_lp = nlsp.AliasCompensatingHammersteinModelLowpass(
input_signal=input_sweep_signal,
nonlin_func=nlsp.function_factory.power_series(i),
max_harm=i,
filter_impulseresponse=filter_ir)
harm_simple = nlsp.get_nl_impulse_response(input_signal,
branch_simple.GetOutput())
harm_up = nlsp.get_nl_impulse_response(input_signal,
branch_up.GetOutput())
harm_lowpass = nlsp.get_nl_impulse_response(input_signal,
branch_lp.GetOutput())
plot.relabelandplot(harm_simple, "simple HM", show=True)
plot.relabelandplot(harm_up, "upsampling HM", show=True)
plot.relabelandplot(harm_lowpass, "lowpass HM", show=True)
print nlsp.harmonicsvsall_energyratio_nl(input_signal,
branch_simple.GetOutput(), i)
print nlsp.harmonicsvsall_energyratio_nl(input_signal,
branch_up.GetOutput(), i)
print nlsp.harmonicsvsall_energyratio_nl(input_signal,
branch_lp.GetOutput(), i)
print
print
def higher_nonlinearity_evaluation():
sampling_rate = 48000
sweep_start_freq = 20.0
sweep_stop_freq = 24000.0
length = 2**15
degree = 5
input_signal = nlsp.NovakSweepGenerator_Sine(
sampling_rate=sampling_rate,
length=length,
start_frequency=sweep_start_freq,
stop_frequency=sweep_stop_freq)
# input_signal = sumpf.modules.SweepGenerator(samplingrate=sampling_rate,length=length)
normal = sumpf.modules.NoiseGenerator.GaussianDistribution(
mean=0.0, standard_deviation=1.0)
wgn_normal = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate,
length=length,
start_frequency=20.0,
stop_frequency=24000.0,
distribution=normal)
frequencies = [
100, 500, 1000, 2000, 4000, 5000, 10000, 15000, 18000, 20000, 24000
]
puretones = nlsp.generate_puretones(frequencies, sampling_rate, length)
input_sweep_signal = input_signal.GetOutput()
model_up_ref = nlsp.AliasCompensatingHammersteinModelUpandDown(
input_signal=input_sweep_signal,
nonlin_func=nlsp.function_factory.power_series(degree),
max_harm=degree)
model_lp_ref = nlsp.AliasCompensatingHammersteinModelLowpass(
input_signal=input_sweep_signal,
nonlin_func=nlsp.function_factory.power_series(degree),
max_harm=degree)
model_up_ref = model_up_ref.GetOutput()
model_lp_ref = model_lp_ref.GetOutput()
for factor in range(degree - 2, degree + 2):
model_up = nlsp.AliasCompensatingHammersteinModelUpandDown(
input_signal=input_sweep_signal,
nonlin_func=nlsp.function_factory.power_series(degree),
max_harm=factor)
model_lp = nlsp.AliasCompensatingHammersteinModelLowpass(
input_signal=input_sweep_signal,
nonlin_func=nlsp.function_factory.power_series(degree),
max_harm=factor)
model_up = model_up.GetOutput()
model_lp = model_lp.GetOutput()
print "Upsampling HM, nonlinearity degree:%r, Alias compensation factor:%r, SNR:%r" % (
degree, factor,
nlsp.snr(model_up_ref, model_up, freqrange=[20.0, 24000.0]))
print "Lowpass HM, nonlinearity degree:%r, Alias compensation factor:%r, SNR:%r" % (
degree, factor,
nlsp.snr(model_lp_ref, model_lp, freqrange=[20.0, 24000.0]))
def reliability_evaluation_sweep():
length = 2**15
for max_harm in range(1, 6):
sweep = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate,
length=length,
start_frequency=20.0,
stop_frequency=2000.0)
ip_signal = sweep.GetOutput()
nl_degree = max_harm
model_simple = nlsp.HammersteinModel(
input_signal=ip_signal,
nonlin_func=nlsp.function_factory.power_series(nl_degree))
model_up = nlsp.AliasCompensatingHammersteinModelUpandDown(
input_signal=ip_signal,
nonlin_func=nlsp.function_factory.power_series(nl_degree),
max_harm=max_harm)
model_lp = nlsp.AliasCompensatingHammersteinModelLowpass(
input_signal=ip_signal,
nonlin_func=nlsp.function_factory.power_series(nl_degree),
max_harm=max_harm)
# plot.relabelandplot(sumpf.modules.FourierTransform(model_simple.GetOutput()).GetSpectrum(),"simple",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(model_up.GetOutput()).GetSpectrum(),"upsampling",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(model_lp.GetOutput()).GetSpectrum(),"lowpass",show=True)
plot.relabelandplot(model_simple.GetOutput(),
"Uncompensated",
show=False)
plot.relabelandplot(model_up.GetOutput(), "Upsampling", show=False)
plot.relabelandplot(model_lp.GetOutput(),
"Lowpass filtering",
show=True)
print "maxharmonics: %r" % max_harm
print "snr between simple HGM and upsampling HGM: %r" % nlsp.snr(
model_simple.GetOutput(), model_up.GetOutput())
print "snr between simple HGM and lowpass HGM: %r" % nlsp.snr(
model_simple.GetOutput(), model_lp.GetOutput())
print "snr between simple HGM and upsampling HGM magnitude: %r" % nlsp.snr_magnitude(
model_simple.GetOutput(), model_up.GetOutput())
print "snr between simple HGM and lowpass HGM magnitude: %r" % nlsp.snr_magnitude(
model_simple.GetOutput(), model_lp.GetOutput())
print
def doublehgm_different_evaluation(input_generator,
branches,
Plot,
reference=None):
input_signal = input_generator.GetOutput()
filter_spec_tofind1 = nlsp.log_bpfilter(branches=branches,
input=input_signal)
filter_spec_tofind2 = nlsp.log_chebyfilter(branches=branches,
input=input_signal)
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(), length=len(input_signal))
ref_nlsystem = nlsp.HammersteinGroup_Series(
input_signal=sweep.GetOutput(),
nonlinear_functions=(nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
nlsp.nl_branches(
nlsp.function_factory.chebyshev1_polynomial,
branches)),
filter_irs=(filter_spec_tofind1, filter_spec_tofind2),
max_harmonics=(range(1, branches + 1), range(1, branches + 1)),
hgm_type=(nlsp.HammersteinGroupModel_up,
nlsp.HammersteinGroupModel_up))
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(2), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs, filter_length)
ref_nlsystem = nlsp.HammersteinGroup_Series(
input_signal=input_signal,
nonlinear_functions=(nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
nlsp.nl_branches(
nlsp.function_factory.chebyshev1_polynomial,
branches)),
filter_irs=(filter_spec_tofind1, filter_spec_tofind2),
max_harmonics=(range(1, branches + 1), range(1, branches + 1)),
hgm_type=(nlsp.HammersteinGroupModel_up,
nlsp.HammersteinGroupModel_up))
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(2),
branches=branches,
init_coeffs=init_coeffs)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem = nlsp.HammersteinGroup_Series(
input_signal=reference,
nonlinear_functions=(
nlsp.nl_branches(nlsp.function_factory.power_series, branches),
nlsp.nl_branches(nlsp.function_factory.chebyshev1_polynomial,
branches)),
filter_irs=(filter_spec_tofind1, filter_spec_tofind2),
max_harmonics=(range(1, branches + 1), range(1, branches + 1)),
hgm_type=(nlsp.HammersteinGroupModel_up,
nlsp.HammersteinGroupModel_up))
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplotphase(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput(2)).GetSpectrum(),
"Reference System",
show=False)
plot.relabelandplotphase(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified System",
show=True)
print "SNR between Reference and Identified output for double hgm different: %r" % nlsp.snr(
ref_nlsystem.GetOutput(2), iden_nlsystem.GetOutput())
sampling_rate = 48000.0
start_freq = 20.0
stop_freq = 20000.0
length = 2**16
fade_out = 0.00
fade_in = 0.00
branches = 3
normal = sumpf.modules.NoiseGenerator.GaussianDistribution(mean=0.0,standard_deviation=1.0)
uniform = sumpf.modules.NoiseGenerator.UniformDistribution()
pink = sumpf.modules.NoiseGenerator.PinkNoise()
laplace = sumpf.modules.NoiseGenerator.LaplaceDistribution(mean=0.0,scale=0.2)
Plot = False
Save = False
sine = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate, length=length, start_frequency=start_freq,
stop_frequency=stop_freq, fade_out= fade_out,fade_in=fade_in)
cos = nlsp.NovakSweepGenerator_Cosine(sampling_rate=sampling_rate, length=length, start_frequency=start_freq,
stop_frequency=stop_freq, fade_out= fade_out,fade_in=fade_in)
wgn_normal = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate, length=length, start_frequency=start_freq,
stop_frequency=stop_freq, distribution=normal)
wgn_uniform = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate, length=length, start_frequency=start_freq,
stop_frequency=stop_freq, distribution=uniform)
wgn_pink = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate, length=length, start_frequency=start_freq,
stop_frequency=stop_freq, distribution=pink)
wgn_laplace = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate, length=length, start_frequency=start_freq,
stop_frequency=stop_freq, distribution=laplace)
excitation = [sine,
sine,
sine,
cos,
def linearmodel_evaluation(input_generator,
branches,
nlfunction,
Plot,
reference=None):
input_signal = input_generator.GetOutput()
prp = sumpf.modules.ChannelDataProperties()
prp.SetSignal(input_signal)
filter_ir = sumpf.modules.FilterGenerator(
filterfunction=sumpf.modules.FilterGenerator.BUTTERWORTH(order=10),
frequency=10000.0,
transform=False,
resolution=prp.GetResolution(),
length=prp.GetSpectrumLength()).GetSpectrum()
ref_nlsystem = nlsp.AliasCompensatingHammersteinModelUpandDown(
filter_impulseresponse=sumpf.modules.InverseFourierTransform(
filter_ir).GetSignal())
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(), length=len(input_signal))
ref_nlsystem.SetInput(sweep.GetOutput())
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
init_coeffs = nlsp.change_length_filterkernels(init_coeffs, filter_length)
ref_nlsystem.SetInput(input_signal)
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
# linear system identification
sweep = nlsp.NovakSweepGenerator_Sine(
length=len(input_signal), sampling_rate=input_signal.GetSamplingRate())
ref_nlsystem.SetInput(sweep.GetOutput())
kernel_linear = nlsp.linear_identification_temporalreversal(
sweep, ref_nlsystem.GetOutput())
iden_linsystem = nlsp.AliasCompensatingHammersteinModelUpandDown(
filter_impulseresponse=kernel_linear)
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem.SetInput(reference)
iden_linsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplot(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference System",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified System",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_linsystem.GetOutput()).GetSpectrum(),
"Identified linear System",
show=True)
print "SNR between Reference and Identified output for linear systems: %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput())
print "SNR between Reference and Identified output for linear systems(linear identification): %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_linsystem.GetOutput())
def robustness_differentexcitation_evaluation(input_generator, branches,
iden_method, Plot):
input_signal = input_generator.GetOutput()
sampling_rate = input_signal.GetSamplingRate()
start_freq = input_generator.GetStartFrequency()
stop_freq = input_generator.GetStopFrequency()
length = len(input_signal)
normal = sumpf.modules.NoiseGenerator.GaussianDistribution(
mean=0.0, standard_deviation=1.0)
uniform = sumpf.modules.NoiseGenerator.UniformDistribution()
pink = sumpf.modules.NoiseGenerator.PinkNoise()
laplace = sumpf.modules.NoiseGenerator.LaplaceDistribution(mean=0.0,
scale=0.3)
sine = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq)
cos = nlsp.NovakSweepGenerator_Cosine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq)
wgn_normal = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
distribution=normal)
wgn_uniform = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
distribution=uniform)
wgn_pink = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
distribution=pink)
wgn_laplace = nlsp.WhiteGaussianGenerator(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
distribution=laplace)
filter_spec_tofind = nlsp.log_weightingfilter(branches=branches,
input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
found_filter_spec, nl_functions = iden_method(input_generator,
ref_nlsystem.GetOutput(),
branches)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
inputs = [sine, cos, wgn_normal, wgn_uniform, wgn_pink, wgn_laplace]
print "SNR between reference and identified output: %r" % (nlsp.snr(
ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput()))
for input in inputs:
ref_nlsystem.SetInput(input.GetOutput())
iden_nlsystem.SetInput(input.GetOutput())
if Plot is True:
nlsp.relabelandplotphase(
sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output Scaled", False)
nlsp.relabelandplot(
sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output", True)
print "SNR between reference and identified output: %r for inputsignal: %r" % (
nlsp.snr(ref_nlsystem.GetOutput(),
iden_nlsystem.GetOutput()), str(input))
print
print
def linearmodel_evaluation(input_generator,
branches,
nlfunction,
iden_method,
Plot,
reference=None):
"""
Evaluation of System Identification method by linear amplification
nonlinear system - no nonlinearity, linear amplifier as linear system
inputsignal - signal signal
plot - the virtual linear system output and the identified linear system output
expectation - utmost similarity between the two outputs
"""
input_signal = input_generator.GetOutput()
prp = sumpf.modules.ChannelDataProperties()
prp.SetSignal(input_signal)
filter_ir = sumpf.modules.FilterGenerator(
filterfunction=sumpf.modules.FilterGenerator.BUTTERWORTH(order=10),
frequency=10000.0,
transform=False,
resolution=prp.GetResolution(),
length=prp.GetSpectrumLength()).GetSpectrum()
ref_nlsystem = nlsp.AliasingCompensatedHM_upsampling(
filter_impulseresponse=sumpf.modules.InverseFourierTransform(
filter_ir).GetSignal())
ref_nlsystem.SetInput(input_signal)
# nonlinear system identification
found_filter_spec, nl_functions = iden_method(input_generator,
ref_nlsystem.GetOutput(),
branches)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
# linear system identification
sweep = nlsp.NovakSweepGenerator_Sine(
length=len(input_signal), sampling_rate=input_signal.GetSamplingRate())
ref_nlsystem.SetInput(sweep.GetOutput())
kernel_linear = nlsp.linear_identification_temporalreversal(
sweep, ref_nlsystem.GetOutput())
iden_linsystem = nlsp.AliasCompensatingHammersteinModelUpandDown(
filter_impulseresponse=kernel_linear)
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem.SetInput(reference)
iden_linsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplot(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference System",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified System",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_linsystem.GetOutput()).GetSpectrum(),
"Identified linear System",
show=True)
print "SNR between Reference and Identified output for linear systems: %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput())
print "SNR between Reference and Identified output for linear systems(linear identification): %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_linsystem.GetOutput())
# normal sweep vs novak's sweep comparison
import sumpf
import nlsp
import nlsp.common.plots as plot
start_frequency = 20.0
stop_frequency = 20000.0
length = 2**15
branches = 3
sampling_rate = 48000
novak_sweep = nlsp.NovakSweepGenerator_Sine(start_frequency=start_frequency, stop_frequency=stop_frequency,
sampling_rate=sampling_rate, length=length)
farina_sweep = nlsp.FarinaSweepGenerator_Sine(start_frequency=start_frequency, stop_frequency=stop_frequency,
sampling_rate=sampling_rate, length=length)
input_signal_novak = novak_sweep.GetOutput()
input_signal_farina = farina_sweep.GetOutput()
filter_spec_tofind_novak = nlsp.log_bpfilter(branches=branches,input=input_signal_novak)
filter_spec_tofind_farina = nlsp.log_bpfilter(branches=branches,input=input_signal_farina)
ref_nlsystem_novak = nlsp.HammersteinGroupModel_up(nonlinear_functions=nlsp.nl_branches(nlsp.function_factory.power_series,branches),
filter_irs=filter_spec_tofind_novak,
max_harmonics=range(1,branches+1))
ref_nlsystem_farina = nlsp.HammersteinGroupModel_up(nonlinear_functions=nlsp.nl_branches(nlsp.function_factory.power_series,branches),
filter_irs=filter_spec_tofind_farina,
max_harmonics=range(1,branches+1))
ref_nlsystem_novak.SetInput(input_signal_novak)
ref_nlsystem_farina.SetInput(input_signal_farina)
ir_novak = nlsp.getnl_ir(novak_sweep,ref_nlsystem_novak.GetOutput(),branches)
ir_farina = nlsp.getnl_ir(farina_sweep,ref_nlsystem_farina.GetOutput(),branches)
def loudspeaker_evaluation_all(branches=3, Plot=True):
branches = branches
sampling_rate = 48000.0
length = 2**18
start_freq = 100.0
stop_freq = 20000.0
fade_out = 0.02
fade_in = 0.02
filter_taps = 2**10
iterations = 5
source_dir = "C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_db/"
# input generator
sine_g = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
fade_out=fade_out,
fade_in=fade_in)
cos_g = nlsp.NovakSweepGenerator_Cosine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
fade_out=fade_out,
fade_in=fade_in)
# real world reference system, load input and output noise and sweeps
load_noise = sumpf.modules.SignalFile(
filename=os.path.join(source_dir, "Noise18.npz"),
format=sumpf.modules.SignalFile.WAV_FLOAT)
load_sine = sumpf.modules.SignalFile(
filename=os.path.join(source_dir, "sine_f.npz"),
format=sumpf.modules.SignalFile.WAV_FLOAT)
load_cosine = sumpf.modules.SignalFile(
filename=os.path.join(source_dir, "cos_f.npz"),
format=sumpf.modules.SignalFile.WAV_FLOAT)
load_sample = sumpf.modules.SignalFile(
filename=os.path.join(source_dir, "Music1.npz"),
format=sumpf.modules.SignalFile.WAV_FLOAT)
output_sample = sumpf.modules.SplitSignal(data=load_sample.GetSignal(),
channels=[1]).GetOutput()
input_sample = sumpf.modules.SplitSignal(data=load_sample.GetSignal(),
channels=[0]).GetOutput()
output_sample = nlsp.change_length_signal(output_sample, 2**18)
input_sample = nlsp.change_length_signal(input_sample, 2**18)
output_noise = sumpf.modules.SplitSignal(data=load_noise.GetSignal(),
channels=[1]).GetOutput()
input_noise = sumpf.modules.SplitSignal(data=load_noise.GetSignal(),
channels=[0]).GetOutput()
output_sine = sumpf.modules.SplitSignal(data=load_sine.GetSignal(),
channels=[1]).GetOutput()
output_cos = sumpf.modules.SplitSignal(data=load_cosine.GetSignal(),
channels=[1]).GetOutput()
impulse = sumpf.modules.ImpulseGenerator(
length=filter_taps, samplingrate=sampling_rate).GetSignal()
# linear identification
kernel_linear, nlfunc = nlsp.linear_identification(sine_g, output_sine,
branches)
iden_nlsystem_linear = nlsp.HammersteinGroupModel_up(
filter_irs=kernel_linear, nonlinear_functions=nlfunc)
# linear identification
kernel_linear_hgm, nlfunc_hgm = nlsp.linear_identification_powerhgm(
sine_g, output_sine, branches)
iden_nlsystem_linear_hgm = nlsp.HammersteinGroupModel_up(
filter_irs=kernel_linear_hgm, nonlinear_functions=nlfunc_hgm)
# only sine based system identification
# found_filter_spec_sine, nl_function_sine = nlsp.systemidentification("LS",nlsp.nonlinearconvolution_powerseries_temporalreversal,
# branches,sine_g,output_sine)
found_filter_spec_sine, nl_function_sine = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sine_g, output_sine, branches)
iden_nlsystem_sine = nlsp.HammersteinGroupModel_up(
max_harmonics=range(1, branches + 1),
nonlinear_functions=nl_function_sine,
filter_irs=found_filter_spec_sine)
# only cosine based system identification
# found_filter_spec_cos, nl_function_cos = nlsp.systemidentification("LS",nlsp.nonlinearconvolution_chebyshev_temporalreversal,
# branches,cos_g,output_cos)
found_filter_spec_cos, nl_function_cos = nlsp.nonlinearconvolution_chebyshev_temporalreversal(
cos_g, output_cos, branches)
iden_nlsystem_cos = nlsp.HammersteinGroupModel_up(
max_harmonics=range(1, branches + 1),
nonlinear_functions=nl_function_cos,
filter_irs=found_filter_spec_cos)
# only noise based system identification
# found_filter_spec_adapt, nl_function_adapt = nlsp.adaptive_identification_legendre(input_generator=input_noise,outputs=output_noise,iterations=iterations,branches=branches,
# step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False)
# iden_nlsystem_adapt = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_adapt,
# filter_irs=found_filter_spec_adapt)
# sine based as init coeff for noise based system identification
# found_filter_spec_sine_reducedlength = nlsp.change_length_filterkernels(found_filter_spec_sine,length=filter_taps)
# found_filter_spec_sineadapt, nl_function_sineadapt = nlsp.adaptive_identification_legendre(input_generator=input_noise,outputs=output_noise,iterations=iterations,branches=branches,
# step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False,init_coeffs=found_filter_spec_sine_reducedlength)
# iden_nlsystem_sineadapt = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_sineadapt,
# filter_irs=found_filter_spec_sineadapt)
# cos based as init coeff for noise based system identification
# found_filter_spec_cos_reducedlength = nlsp.change_length_filterkernels(found_filter_spec_cos,length=filter_taps)
# found_filter_spec_cosadapt, nl_function_cosadapt = nlsp.adaptive_identification_powerseries(input_generator=input_noise,outputs=output_noise,iterations=iterations,branches=branches,
# step_size=0.1,filtertaps=filter_taps,algorithm=nlsp.multichannel_nlms,Plot=False,init_coeffs=found_filter_spec_cos_reducedlength,
# nonlinear_func=nlsp.function_factory.chebyshev1_polynomial)
# iden_nlsystem_cosadapt = nlsp.HammersteinGroupModel_up(max_harmonics=range(1,branches+1),nonlinear_functions=nl_function_cosadapt,
# filter_irs=found_filter_spec_cosadapt)
# set excitation as input
iden_nlsystem_linear.SetInput(sine_g.GetOutput())
iden_nlsystem_linear_hgm.SetInput(sine_g.GetOutput())
iden_nlsystem_sine.SetInput(sine_g.GetOutput())
iden_nlsystem_cos.SetInput(cos_g.GetOutput())
# iden_nlsystem_adapt.SetInput(input_noise)
# iden_nlsystem_sineadapt.SetInput(input_noise)
# iden_nlsystem_cosadapt.SetInput(input_noise)
plot.relabelandplot(
sumpf.modules.FourierTransform(output_sine).GetSpectrum(),
"Reference system",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem_sine.GetOutput()).GetSpectrum(),
"Sweep based system identification",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem_linear.GetOutput()).GetSpectrum(),
"Linear identified system",
show=True)
if Plot is True:
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_sine.GetOutput()).GetSpectrum(),"Identified power",show=False)
plot.relabelandplot(
sumpf.modules.FourierTransform(output_sine).GetSpectrum(),
"Reference system",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem_linear.GetOutput()).GetSpectrum(),
"Linear identified system",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem_sineadapt.GetOutput()).GetSpectrum(),
"sweep-adaptive output",
show=True)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_cos.GetOutput()).GetSpectrum(),"Identified cheby",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(output_cos).GetSpectrum(),"Reference cheby",show=True)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_adapt.GetOutput()).GetSpectrum(),"Identified Adapt noise",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_sineadapt.GetOutput()).GetSpectrum(),"Identified power Adapt noise",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_cosadapt.GetOutput()).GetSpectrum(),"Identified chebyshev Adapt noise",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(output_noise).GetSpectrum(),"Reference Adapt noise",show=True)
print "SNR between Reference and Identified output linear: %r" % nlsp.snr(
output_sine, iden_nlsystem_linear.GetOutput())
print "SNR between Reference and Identified output linear hgm: %r" % nlsp.snr(
output_sine, iden_nlsystem_linear_hgm.GetOutput())
print "SNR between Reference and Identified output Powerseries NL convolution: %r" % nlsp.snr(
output_sine, iden_nlsystem_sine.GetOutput())
print "SNR between Reference and Identified output Chebyshev NL convolution: %r" % nlsp.snr(
output_cos, iden_nlsystem_cos.GetOutput())
print "SNR between Reference and Identified output Adaptive: %r" % nlsp.snr(
output_noise, iden_nlsystem_adapt.GetOutput())
print "SNR between Reference and Identified output Powerseries Adaptive: %r" % nlsp.snr(
output_noise, iden_nlsystem_sineadapt.GetOutput())
# print "SNR between Reference and Identified output Chebyshev Adaptive: %r" %nlsp.snr(output_noise, iden_nlsystem_cosadapt.GetOutput())
# set sample as input
iden_nlsystem_linear.SetInput(input_sample)
iden_nlsystem_linear_hgm.SetInput(input_sample)
iden_nlsystem_sine.SetInput(input_sample)
iden_nlsystem_cos.SetInput(input_sample)
iden_nlsystem_adapt.SetInput(input_sample)
iden_nlsystem_sineadapt.SetInput(input_sample)
# iden_nlsystem_cosadapt.SetInput(input_sample)
# if Plot is True:
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_sine.GetOutput()).GetSpectrum(),"Identified power",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_cos.GetOutput()).GetSpectrum(),"Identified cheby",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_adapt.GetOutput()).GetSpectrum(),"Identified Adapt noise",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_sineadapt.GetOutput()).GetSpectrum(),"Identified power Adapt noise",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(iden_nlsystem_cosadapt.GetOutput()).GetSpectrum(),"Identified chebyshev Adapt noise",show=False)
# plot.relabelandplot(sumpf.modules.FourierTransform(output_sample).GetSpectrum(),"Reference Adapt noise",show=True)
print "SNR between Reference and Identified output linear: %r" % nlsp.snr(
output_sample, iden_nlsystem_linear.GetOutput())
print "SNR between Reference and Identified output linear hgm: %r" % nlsp.snr(
output_sample, iden_nlsystem_linear_hgm.GetOutput())
print "SNR between Reference and Identified output Powerseries NL convolution: %r" % nlsp.snr(
output_sample, iden_nlsystem_sine.GetOutput())
print "SNR between Reference and Identified output Chebyshev NL convolution: %r" % nlsp.snr(
output_sample, iden_nlsystem_cos.GetOutput())
print "SNR between Reference and Identified output Adaptive: %r" % nlsp.snr(
output_sample, iden_nlsystem_adapt.GetOutput())
print "SNR between Reference and Identified output Powerseries Adaptive: %r" % nlsp.snr(
output_sample, iden_nlsystem_sineadapt.GetOutput())
def hgmwithfilter_evaluation_sweepadaptive(input_generator,
branches,
nlfuntion,
Plot,
reference=None):
input_signal = input_generator.GetOutput()
# filter_spec_tofind = nlsp.create_bpfilter([2000,8000,30000],input_signal)
filter_spec_tofind = nlsp.log_bpfilter(branches=branches,
input=input_signal)
# filter_spec_tofind = nlsp.log_chebyfilter(branches=branches,input=input_signal)
ref_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nlsp.nl_branches(nlfuntion, branches),
filter_irs=filter_spec_tofind,
max_harmonics=range(1, branches + 1))
sweep = nlsp.NovakSweepGenerator_Sine(
sampling_rate=input_signal.GetSamplingRate(), length=len(input_signal))
ref_nlsystem.SetInput(sweep.GetOutput())
sweep_start = time.clock()
init_coeffs, non = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sweep, ref_nlsystem.GetOutput(), branches=branches)
sweep_stop = time.clock()
init_coeffs = nlsp.change_length_filterkernels(init_coeffs, filter_length)
ref_nlsystem.SetInput(input_signal)
adapt_sweep_start = time.clock()
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
init_coeffs=init_coeffs,
filtertaps=filter_length)
adapt_sweep_stop = time.clock()
adapt_start = time.clock()
found_filter_spec, nl_functions = nlsp.adaptive_identification_legendre(
input_generator=input_generator,
outputs=ref_nlsystem.GetOutput(),
branches=branches,
filtertaps=filter_length)
adapt_stop = time.clock()
print "sweep_identification time %r" % (sweep_start - sweep_stop)
print "sweep_adapt_identification time %r" % (adapt_sweep_start -
adapt_sweep_stop)
print "adapt_identification time %r" % (adapt_start - adapt_stop)
iden_nlsystem = nlsp.HammersteinGroupModel_up(
input_signal=input_signal,
nonlinear_functions=nl_functions,
filter_irs=found_filter_spec,
max_harmonics=range(1, branches + 1))
# nlsp.filterkernel_evaluation_plot(filter_spec_tofind,found_filter_spec)
# nlsp.filterkernel_evaluation_sum(filter_spec_tofind,found_filter_spec)
if reference is not None:
reference = nlsp.change_length_signal(reference,
length=len(input_signal))
ref_nlsystem.SetInput(reference)
iden_nlsystem.SetInput(reference)
if Plot is True:
plot.relabelandplot(sumpf.modules.FourierTransform(
ref_nlsystem.GetOutput()).GetSpectrum(),
"Reference Output",
show=False)
plot.relabelandplot(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Output",
show=True)
print "SNR between Reference and Identified output without overlapping filters: %r" % nlsp.snr(
ref_nlsystem.GetOutput(), iden_nlsystem.GetOutput())
def wgnasreference():
branches = 3
sampling_rate = 48000.0
length = 2**18
start_freq = 100.0
stop_freq = 20000.0
fade_out = 0.00
fade_in = 0.00
filter_taps = 2**11
# input generator
sine_g = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
fade_out=fade_out,
fade_in=fade_in)
cos_g = nlsp.NovakSweepGenerator_Cosine(sampling_rate=sampling_rate,
length=length,
start_frequency=start_freq,
stop_frequency=stop_freq,
fade_out=fade_out,
fade_in=fade_in)
# real world reference system, load input and output noise and sweeps
load_noise = sumpf.modules.SignalFile(
filename=
"C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_ls/Noise18.npz",
format=sumpf.modules.SignalFile.WAV_FLOAT)
load_sine = sumpf.modules.SignalFile(
filename="C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_ls/sine.npz",
format=sumpf.modules.SignalFile.WAV_FLOAT)
load_cosine = sumpf.modules.SignalFile(
filename="C:/Users/diplomand.8/Desktop/nl_recordings/rec_4_ls/cos.npz",
format=sumpf.modules.SignalFile.WAV_FLOAT)
output_noise = sumpf.modules.SplitSignal(data=load_noise.GetSignal(),
channels=[1]).GetOutput()
input_noise = sumpf.modules.SplitSignal(data=load_noise.GetSignal(),
channels=[0]).GetOutput()
output_sine = sumpf.modules.SplitSignal(data=load_sine.GetSignal(),
channels=[1]).GetOutput()
output_cos = sumpf.modules.SplitSignal(data=load_cosine.GetSignal(),
channels=[1]).GetOutput()
impulse = sumpf.modules.ImpulseGenerator(
length=filter_taps, samplingrate=sampling_rate).GetSignal()
# initialize hgm
iden_nlsystem = nlsp.HammersteinGroupModel_up(
max_harmonics=range(1, branches + 1),
nonlinear_functions=nlsp.nl_branches(
nlsp.function_factory.power_series, branches),
filter_irs=[
impulse,
] * branches)
# only sine based system identification
found_filter_spec_sine, nl_function_sine = nlsp.nonlinearconvolution_powerseries_temporalreversal(
sine_g, output_sine, branches)
iden_nlsystem.SetInput(signal=input_noise)
iden_nlsystem.SetNLFunctions(nl_function_sine)
iden_nlsystem.SetFilterIRS(found_filter_spec_sine)
plot.relabelandplotphase(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified power noise",
show=False)
print "SNR between Reference and Identified output Sine: %r" % nlsp.snr(
output_noise, iden_nlsystem.GetOutput())
# only cosine based system identification
found_filter_spec_cos, nl_function_cos = nlsp.nonlinearconvolution_chebyshev_temporalreversal(
cos_g, output_cos, branches)
iden_nlsystem.SetInput(signal=input_noise)
iden_nlsystem.SetNLFunctions(nl_function_cos)
iden_nlsystem.SetFilterIRS(found_filter_spec_cos)
plot.relabelandplotphase(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified cheby noise",
show=False)
print "SNR between Reference and Identified output Cos: %r" % nlsp.snr(
output_noise, iden_nlsystem.GetOutput())
# only noise based system identification
found_filter_spec_adapt, nl_function_adapt = nlsp.adaptive_identification(
input_generator=input_noise,
outputs=output_noise,
iterations=1,
branches=branches,
step_size=0.1,
filtertaps=filter_taps,
algorithm=nlsp.multichannel_nlms,
Plot=False)
iden_nlsystem.SetInput(signal=input_noise)
iden_nlsystem.SetNLFunctions(nl_function_adapt)
iden_nlsystem.SetFilterIRS(found_filter_spec_adapt)
plot.relabelandplotphase(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified Adapt noise",
show=False)
print "SNR between Reference and Identified output Adapt: %r" % nlsp.snr(
output_noise, iden_nlsystem.GetOutput())
# sine based as init coeff for noise based system identification
found_filter_spec_sine_reducedlength = nlsp.change_length_filterkernels(
found_filter_spec_sine, length=filter_taps)
found_filter_spec_sineadapt, nl_function_sineadapt = nlsp.adaptive_identification(
input_generator=input_noise,
outputs=output_noise,
iterations=1,
branches=branches,
step_size=0.1,
filtertaps=filter_taps,
algorithm=nlsp.multichannel_nlms,
Plot=False,
init_coeffs=found_filter_spec_sine_reducedlength)
iden_nlsystem.SetInput(signal=input_noise)
iden_nlsystem.SetNLFunctions(nl_function_sineadapt)
iden_nlsystem.SetFilterIRS(found_filter_spec_sineadapt)
plot.relabelandplotphase(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified power Adapt noise",
show=False)
print "SNR between Reference and Identified output Sine Adapt: %r" % nlsp.snr(
output_noise, iden_nlsystem.GetOutput())
# cos based as init coeff for noise based system identification
found_filter_spec_cos_reducedlength = nlsp.change_length_filterkernels(
found_filter_spec_cos, length=filter_taps)
found_filter_spec_cosadapt, nl_function_cosadapt = nlsp.adaptive_identification(
input_generator=input_noise,
outputs=output_noise,
iterations=1,
branches=branches,
step_size=0.1,
filtertaps=filter_taps,
algorithm=nlsp.multichannel_nlms,
Plot=False,
init_coeffs=found_filter_spec_cos_reducedlength,
nonlinear_func=nlsp.function_factory.chebyshev1_polynomial)
iden_nlsystem.SetInput(signal=input_noise)
iden_nlsystem.SetNLFunctions(nl_function_cosadapt)
iden_nlsystem.SetFilterIRS(found_filter_spec_cosadapt)
plot.relabelandplotphase(sumpf.modules.FourierTransform(
iden_nlsystem.GetOutput()).GetSpectrum(),
"Identified chebyshev Adapt noise",
show=False)
print "SNR between Reference and Identified output Cos Adapt: %r" % nlsp.snr(
output_noise, iden_nlsystem.GetOutput())
plot.relabelandplotphase(
sumpf.modules.FourierTransform(output_noise).GetSpectrum(),
"Reference Noise",
show=True)
sampling_rate = 48000
sweep_start_freq = 20.0
sweep_stop_freq = 20000.0
degree = 5
puretone_freq = 10000
length = 2**15
Plot = False
silence_duration = 0.00
fade_out = 0.00
fade_in = 0.00
Plot = True
input_signal = nlsp.NovakSweepGenerator_Sine(sampling_rate=sampling_rate,
length=length,
start_frequency=sweep_start_freq,
stop_frequency=sweep_stop_freq,
fade_in=fade_in,
fade_out=fade_out)
input_sweep_signal = input_signal.GetOutput()
# lowpass_evaluation()
# theoretical_evaluation()
# puretone_evaluation()
# sweep_evaluation()
# harmonics_evaluation()
# higher_nonlinearity_evaluation()
# wgn_evaluation()
# linearity_evaluation()
# reliability_evaluation_puretone()
reliability_evaluation_sweep()
# reliability_evaluation_noise()
