fs = 44100
fstart = 1
fstop = 22050
pad = 7
# Excitation signal
excitation = np.zeros(44100)
excitation[0] = 1
excitation_zeropadded = generation.zero_padding(excitation, pad, fs)
system_response = measurement_chain.convolution(ir_imitation.diracs([0, 1050]))(excitation_zeropadded)
h = calculation.deconv_process(excitation,
system_response, fs)[:len(excitation)]
# Plot impulse response
plotting.plot_time(h)
plt.xlim(-500, 10000)
plt.xticks([0, 1050, 2000, 4000, 6000, 8000, 10000])
plt.savefig('impulse_response.png')
plt.close()
# Plot frequency response
duration = 1 pad = 4 # Generate excitation signal excitation = generation.log_sweep(fstart, fstop, duration, fs) N = len(excitation) # Noise in measurement chain noise_level_db = -30 noise = measurement_chain.additive_noise(noise_level_db) # FIR-Filter-System dirac_system = measurement_chain.convolution([1.0]) # Combinate system elements system = measurement_chain.chained(dirac_system, noise) # Lists beta = 7 fade_in_list = np.arange(0, 1001, 1) fade_out = 0 # Spectrum of dirac for reference dirac = np.zeros(pad * fs) dirac[0] = 1 dirac_f = np.fft.rfft(dirac)
import ir_imitation
fs = 44100
fstart = 1
fstop = 22050
pad = 7
# Noise in measurement chain
noise_level_db = -50.
noise = measurement_chain.additive_noise(noise_level_db)
# FIR-Filter-System
dirac_system = measurement_chain.convolution(ir_imitation.diracs([0, 1050]))
# Combinate system elements
system = measurement_chain.chained(dirac_system, noise)
excitation = np.zeros(44100)
excitation[0] = 1
excitation_zeropadded = generation.zero_padding(excitation, pad, fs)
system_response = system(excitation_zeropadded)
h = calculation.deconv_process(excitation, system_response,
fs)[:len(excitation)]
# Plot impulse response