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
hp_filter = 1 # Enable high - pass filter
T = 24000 # Normalised Simulation Time
L = 800 # Set Filter Length
mu = 0.00001 # Set Learning Rate
#
print "Generating Input Signal"
x = GenerateSignal(T, Fs)
#
t1 = time.time()
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