def ParallelProcessing(location):
Antilocation = location[1:4]
P, S = ComputeRIRs(c, Fs, ReceiverLoc, SourceLoc, Antilocation, RoomSize,
beta, nsample, mtype, order, dim, orientation,
hp_filter)
e_cont, Yd = ANCInAction(P, S, x, T, L, mu)
Pd_dB, fd, Pe_dB, fe, EstimatedAttenuation, components = metrics(
e_cont, Yd, Fs, T)
features = np.append(Antilocation, EstimatedAttenuation)
features = np.append(features, components)
return features
def ParallelProcessing(location):
SourceLoc = location[0:3]
ReceiverLoc = location[3:6]
Antilocation = location[6:9]
P, S = ComputeRIRs(c,Fs,ReceiverLoc,SourceLoc,Antilocation,RoomSize,beta,nsample,mtype,order,dim,orientation,hp_filter)
PSpecFlat = SpectralFlatness(P, Fs)
SSpecFlat = SpectralFlatness(S, Fs)
e_cont, Yd = ANCInAction(P, S, x, T, L, mu)
# Pd_dB, fd, Pe_dB, fe, EstimatedAttenuation, components = metrics(e_cont, Yd, Fs, T)
ANCRed = metrics(e_cont, Yd, Fs, T)
features = np.append(location, [PSpecFlat, SSpecFlat, ANCRed])
return features
print "Computing RIRs"
P, S = ComputeRIRs(c, Fs, ReceiverLoc, SourceLoc, AntiNoiseLoc,
RoomSize, beta, nsample, mtype, order, dim,
orientation, hp_filter)
print "Simulating ANC"
error, Yd = ANCInAction(P, S, x, T, L, mu)
# Pd_dB, fd, Pe_dB, fe, attenuation, components\
# = metrics(error, Yd, Fs, T)# print "Estimated \
# Attenuation is : ", attenuation, components
ANCRed = metrics(error, Yd, Fs, T)
#
# Generate Dynamic Power Spectral Density Graphs
TimeDynamics(
Pd_dB,
fd,
Pe_dB,
fe,
attenuation,
SourceLoc,
ReceiverLoc,
AntiNoiseLoc)
RIRTimeDomain(P, Fs)
EnergyDecayCurve(P, Fs, S)
#
signal_das = mics.process(FD=False)
signal_das = normalize(signal_das)
centerMicSignal = room_bf.mic_array.signals[-1, :]
centerMicSignal = normalize(centerMicSignal)
#
P, S = room_bf.rir[0]
P = P[:rirlen]
P = normalize(P)
S = S[:rirlen]
S = normalize(S)
error_b, Yd_b = ANCInActionBeamer(P, S, xtone, centerMicSignal, T * 8000,
L, mu)
error, Yd = ANCInAction(P, S, xtone, T * 8000, L, mu)
ANCRedB = metrics(error_b, Yd_b, Fs, T * 8000)
ANCRed = metrics(error, Yd, Fs, T * 8000)
result = result.append(
{
'Alpha': alpha,
'ANCRed': ANCRed,
'ANCRedBeam': ANCRedB
},
ignore_index=True)
print alpha, ANCRed, ANCRedB
# plot the room and resulting beamformer before simulation
fig, ax = room_bf.plot(freq=[300, 450, 500], img_order=1)
ax.legend(['300', '450', '500'])
plt.xlabel('x')
# sigma2_n*np.eye(mics.Lg*mics.M))
# output = mics.process()
# out = pra.normalize(pra.highpass(output, Fs))
# input_mic = pra.normalize(pra.highpass(mics.signals[mics.M//2], Fs))
# input_mic = normalize(input_mic)
P, S = roomPoly.rir[0]
P = P[:rirlen]
P = normalize(P)
S = S[:rirlen]
P = normalize(S)
error_b, Yd_b = ANCInActionBeamer(P, S, xtone, out, T * 8000, L, mu)
error, Yd = ANCInAction(P, S, xtone, T * 8000, L, mu)
Pd_dBB, fdB, Pe_dBB, feB, ANCRedB = metrics(error_b, Yd_b, Fs, T * 8000)
Pd_dB, fd, Pe_dB, fe, ANCRed = metrics(error, Yd, Fs, T * 8000)
result = result.append(
{
'Alpha': alpha,
'ANCRed': ANCRed,
'ANCRedBeam': ANCRedB
},
ignore_index=True)
print alpha, ANCRed, ANCRedB
TimeDynamics(Pd_dBB, fdB, Pe_dBB, feB, ANCRedB)
fig, ax = roomPoly.plot(freq=[280, 320, 350], img_order=1)
ax.legend(['300', '450', '500'])