[0.5], np.zeros(11), [-0.3], np.zeros(3),
[0.1], np.zeros(20), [-0.05]))
d = np.convolve(u, coeffs)
# Add background noise
v = np.random.randn(len(d)) * np.sqrt(5000)
d += v
# Apply adaptive filter
M = 100 # Number of filter taps in adaptive filter
step = 0.1 # Step size
y, e, w = adf.nlms(u, d, M, step, returnCoeffs=True)
# Calculate mean square weight error
mswe = adf.mswe(w, coeffs)
# Plot speech signals
plt.figure()
plt.title("Speech signals")
plt.plot(u, label="Emily's speech signal, u(n)")
plt.plot(d, label="Speech signal from John, d(n)")
plt.grid()
plt.legend()
plt.xlabel('Samples')
# Plot error signal - note how the measurement noise affects the error
plt.figure()
plt.title('Error signal e(n)')
plt.plot(e)
plt.grid()
d = np.round(d,0)
# Hear how the mixed signal sounds before proceeding with the filtering.
dsound = d.astype('int16')
wavfile.write(waveout, lfs, dsound)
music = pyglet.resource.media('output.wav')
music.play()
time.sleep(len(dsound)/lfs)
# Apply adaptive filter
y, e, w = adf.nlms(snd[:len(d)], d, tap, step, returnCoeffs=True)
# The algorithm stores the processed result in the variable 'e', which is the mix of the error signal and the listener's voice.
# Hear how e sounds now. Ideally we on behalf of the sender, should hear only the listener's voice. Practically, some echo would still be present.
e = e.astype('int16')
wavfile.write('adapt.wav', lfs, e)
music = pyglet.resource.media(filtout)
music.play()
time.sleep(len(e)/lfs)
# Calculate and plot the mean square weight error
mswe = adf.mswe(w, coeffs)
plt.figure()
plt.title('Mean squared weight error')
plt.plot(mswe)
plt.grid()
plt.xlabel('Samples')
plt.show()
coeffs = np.concatenate(([-4, 3.2], np.zeros(20), [0.7], np.zeros(33), [-0.1])) u = np.random.randn(N) d = np.convolve(u, coeffs) # Perform filtering M = 60 # No. of taps to estimate mu1 = 0.0008 # Step size 1 in LMS mu2 = 0.0004 # Step size 1 in LMS beta1 = 0.08 # Step size 2 in NLMS and AP beta2 = 0.04 # Step size 2 in NLMS and AP K = 3 # Projection order 1 in AP # LMS y_lms1, e_lms1, w_lms1 = adf.lms(u, d, M, mu1, returnCoeffs=True) y_lms2, e_lms2, w_lms2 = adf.lms(u, d, M, mu2, returnCoeffs=True) mswe_lms1 = adf.mswe(w_lms1, coeffs) mswe_lms2 = adf.mswe(w_lms2, coeffs) # NLMS y_nlms1, e_nlms1, w_nlms1 = adf.nlms(u, d, M, beta1, returnCoeffs=True) y_nlms2, e_nlms2, w_nlms2 = adf.nlms(u, d, M, beta2, returnCoeffs=True) mswe_nlms1 = adf.mswe(w_nlms1, coeffs) mswe_nlms2 = adf.mswe(w_nlms2, coeffs) # AP y_ap1, e_ap1, w_ap1 = adf.ap(u, d, M, beta1, K, returnCoeffs=True) y_ap2, e_ap2, w_ap2 = adf.ap(u, d, M, beta2, K, returnCoeffs=True) mswe_ap1 = adf.mswe(w_ap1, coeffs) mswe_ap2 = adf.mswe(w_ap2, coeffs) # Plot results