signal = np.random.normal(0, 1, size=n_buffers * blocklength)
# the adaptive filter
filt = FastBlockLMSFilter(length, blocklength, stepsize=0.1, leakage=0.9999)
# secondary path estimate has to account for block size
plant_model = FIRFilter(np.concatenate((np.zeros(blocklength), h_sec)))
# simulates an audio interface with primary and secondary paths and 40 dB SNR noise
# at the error sensor
sim = FakeInterface(
blocklength,
signal,
h_pri=h_pri,
h_sec=h_sec,
noise=wgn(olafilt(h_pri, signal), 40, "dB"),
)
elog = []
y = np.zeros(blocklength) # control signal is zero for first block
for i in range(n_buffers):
# record reference signal x and error signal e while playing back y
x, e, _, _ = sim.playrec(-y)
# filter the reference signal
fx = plant_model(x)
# adapt filter
filt.adapt(fx, e)
# filter
y = filt.filt(x)
# log error
elog.append(e)
length = 64 # number of adaptive FIR filter taps
blocklength = 4 # length of I/O buffer and blocksize of filter
n_buffers = 10000 # size of simulation
estimation_phase = 2000
# primary and secondary paths
h_pri = np.zeros(64)
h_pri[60] = 1
h_sec = np.zeros(64)
h_sec[20] = 1
# simulates an audio interface with primary and secondary paths and 40 dB SNR noise
# at the error sensor
signal = np.random.normal(0, 1, size=n_buffers * blocklength)
sim = FakeInterface(
blocklength, signal, h_pri=h_pri, h_sec=h_sec, noise=wgn(signal, 20, "dB")
)
# the adaptive filter
filt = FastBlockLMSFilter(
length, blocklength, stepsize=0.01, leakage=0.99999, power_averaging=0.9
)
filt.locked = True
# secondary path estimate has to account for block size
plant_model = FIRFilter(np.zeros(blocklength + length))
# adaptive plant model
adaptive_plant_model = FastBlockLMSFilter(
length, blocklength, stepsize=0.01, leakage=0.99999
)