K_true = 0
for k in range(0, len(micList)):
K_true += signalsPower[k] * distance(
micList[k], source_pos) * distance(micList[k], source_pos)
K_true /= len(micList)
# Calculate K estimation
K_estim_exakt = list()
K_estim_noise = list()
for i in range(0, len(micList)):
for j in range(0, len(micList)):
if (i != j):
a = getRealTDOA(source_pos, micList[i], micList[j])
b = getFakeTDOA(a, 96000)
K1_exakt, K2_exakt = estimateK_Pair(
signalsPower[i], signalsPower[j], micList[i],
micList[j], a * 343.2)
K1_noised, K2_noised = estimateK_Pair(
signalsPower[i], signalsPower[j], micList[i],
micList[j], b * 343.2)
K_estim_exakt.append(K1_exakt)
K_estim_exakt.append(K2_exakt)
K_estim_noise.append(K1_noised)
K_estim_noise.append(K2_noised)
# Remove NAN
K_estim_exakt = [x for x in K_estim_exakt if str(x) != 'nan']
K_estim_noise = [x for x in K_estim_noise if str(x) != 'nan']
K_estim_exakt.sort()
K_estim_noise.sort()
K_estim = K_estim_noise[int(len(K_estim_noise) * xval)]
delta_t2 = delta_n2*meta["sampling_spacing"] delta_t3 = delta_n3*meta["sampling_spacing"] delta_t4 = delta_n4*meta["sampling_spacing"] delta_t5 = delta_n5*meta["sampling_spacing"] delta_t6 = delta_n6*meta["sampling_spacing"] delta_s1 = delta_t1*343.2 delta_s2 = delta_t2*343.2 delta_s3 = delta_t3*343.2 delta_s4 = delta_t4*343.2 delta_s5 = delta_t5*343.2 delta_s6 = delta_t6*343.2 # AMP Verfahren K_list = list() R_listA = list() K1, K2 = estimateK_Pair(powerA, powerB, micA_pos, micB_pos, delta_s1) K_list.append(K1) K_list.append(K2) R_listA.append(K1/np.sqrt(powerA)) R_listA.append(K2/np.sqrt(powerA)) K1, K2 = estimateK_Pair(powerA, powerC, micA_pos, micC_pos, delta_s2) K_list.append(K1) K_list.append(K2) R_listA.append(K1/np.sqrt(powerA)) R_listA.append(K2/np.sqrt(powerA)) K1, K2 = estimateK_Pair(powerA, powerD, micA_pos, micD_pos, delta_s3) K_list.append(K1) K_list.append(K2) R_listA.append(K1/np.sqrt(powerA)) R_listA.append(K2/np.sqrt(powerA)) K1, K2 = estimateK_Pair(powerB, powerC, micB_pos, micC_pos, delta_s4)
tdoa = basic_tdoa.BasicTDOA(loaded, 0, 0.0, arr)
TDOA_mat = tdoa.tdoa_gcc_phat(0.0)[1][0]
K_list = list()
for i in range(0,7):
for j in range(0,7):
if(i!=j):
delta_n1 = TDOA_mat[j][i] # MIC A and MIC B
delta_t1 = delta_n1*meta["sampling_spacing"]
delta_s1 = delta_t1*343.2
powerA = getSignalPower_UsingTime_AverageFree(np.asarray(signals[i]))
powerB = getSignalPower_UsingTime_AverageFree(np.asarray(signals[j]))
micA_pos = convertPoint(config["microphone_positions"][i])
micB_pos = convertPoint(config["microphone_positions"][j])
K1, K2 = estimateK_Pair(powerA, powerB, micA_pos, micB_pos, delta_s1)
K_list.append(K1)
K_list.append(K2)
# AMP Verfahren
FILTER_LOW = int(len(K_list)*0.4)
FILTER_HIGH = len(K_list)-1
K_list = [x for x in K_list if str(x) != 'nan']
K_list.sort()
K_list_filt = [x for x in K_list if x > 0.05] #K_list[FILTER_LOW-1:FILTER_HIGH]
estim_K = np.median(K_list_filt)
import matplotlib.pyplot as plt
#plt.plot(K_list)
#
z = np.asarray(s)
power = getSignalPower_UsingTime_AverageFree(np.asarray(s))
powers.append(power)
# print("microphone ",power,"W")
#print("Source Power ",getSignalPower_UsingTime_AverageFree(source_signal),"W")
# Calculate TDOA
arr = array_parameters.ArrayParameters(config["microphone_positions"])
tdoa = basic_tdoa.BasicTDOA(loaded, 0, 0.0, arr)
# Do Amplitude Verfahren
powerA = powers[0]
powerB = powers[1]
micA_pos = convertPoint(config["microphone_positions"][0])
micB_pos = convertPoint(config["microphone_positions"][1])
dTDOA = tdoa.tdoa_gcc_phat(0.0)[1][0][1][0]
dSDOA = dTDOA * meta["sampling_spacing"] * 343.2
K1, K2 = estimateK_Pair(powerA, powerB, micA_pos, micB_pos, dSDOA)
rA_1 = K1 / np.sqrt(powerA)
rA_2 = K2 / np.sqrt(powerA)
rB_1 = K1 / np.sqrt(powerB)
rB_2 = K2 / np.sqrt(powerB)
solutions = getIntersectionPointsCircle(micA_pos, rA_2, micB_pos, rB_2)
estimPos = getPlausibleOne(solutions[0], solutions[1])
sourcePos = convertPoint(config["source_position"])
print(i, config["source_position"], estimPos, " ", sourcePos, " > ",
distance(estimPos, sourcePos))
