def calculateSNR(arr, sig, dis, angl, time_windowNoise, time_windowSignal):
# Lade Daten
data, meta_data = load_measurement(arr, sig, angl, dis)
pNoise = list()
pSigna = list()
snr_FC = list()
snr_DB = list()
for channel in [1, 2, 3, 4, 5, 6, 7, 8]:
# Für jedes Mikrofon die Signalleistungen Bestimmen
signal = data[channel]
# Schneide Aufnahmen
time2, signal2 = cutSignal(signal, meta_data, time_windowNoise)
time3, signal3 = cutSignal(signal, meta_data, time_windowSignal)
pNoise.append(getSignalPower_UsingTime_AverageFree(signal2))
pSigna.append(
getSignalPower_UsingTime_AverageFree(signal3) -
getSignalPower_UsingTime_AverageFree(signal2))
snr_FC.append((getSignalPower_UsingTime_AverageFree(signal3) -
getSignalPower_UsingTime_AverageFree(signal2)) /
(getSignalPower_UsingTime_AverageFree(signal2)))
snr_DB.append(10 * np.log(
(getSignalPower_UsingTime_AverageFree(signal3) -
getSignalPower_UsingTime_AverageFree(signal2)) /
(getSignalPower_UsingTime_AverageFree(signal2))))
return pNoise, pSigna, snr_FC, snr_DB
def calculateSNR(data, meta_data, time_windowNoise, time_windowSignal):
# Lade Daten
pNoise = list()
pSigna = list()
snr_FC = list()
snr_DB = list()
K_values = [
1.0000000, 1.12685472, 1.06518856, 1.47356999, 1.19323192, 1.54429486,
1.38300818, 1.21535447
]
for channel in [1, 2, 3, 4, 5, 6, 7, 8]:
# Für jedes Mikrofon die Signalleistungen Bestimmen
signal = data[channel]
# Schneide Aufnahmen
time2, signal2 = cutSignal(signal, meta_data, time_windowNoise)
time3, signal3 = cutSignal(signal, meta_data, time_windowSignal)
pNoise.append(K_values[channel - 1] *
getSignalPower_UsingTime_AverageFree(signal2))
pSigna.append(K_values[channel - 1] *
getSignalPower_UsingTime_AverageFree(signal3) -
getSignalPower_UsingTime_AverageFree(signal2))
snr_FC.append((getSignalPower_UsingTime_AverageFree(signal3) -
getSignalPower_UsingTime_AverageFree(signal2)) /
(getSignalPower_UsingTime_AverageFree(signal2)))
snr_DB.append(10 * np.log(
(getSignalPower_UsingTime_AverageFree(signal3) -
getSignalPower_UsingTime_AverageFree(signal2)) /
(getSignalPower_UsingTime_AverageFree(signal2))))
return pNoise, pSigna, snr_FC, snr_DB
def getSNR(signal):
mx = np.max(signal)
thrFil = np.asarray([signal > mx * 0.9])
fromIdx = np.where(thrFil == True)[1][0]
toIdx = thrFil.shape[1] - np.where(np.flip(thrFil) == True)[1][0]
time_window_signal = {"from": fromIdx, "to": toIdx}
sig_cut = cutSignal_sample(signal, meta, time_window_signal)
time_window_signal = {"from": 0, "to": 2000}
noi_cut = cutSignal_sample(signal, meta, time_window_signal)
powerSig = getSignalPower_UsingTime_AverageFree(sig_cut[1])
powerNoi = getSignalPower_UsingTime_AverageFree(noi_cut[1])
snrFac = (powerSig - powerNoi) / (powerNoi)
snrDB = 10 * np.log(snrFac)
return powerSig, powerNoi, snrFac, snrDB
micD_pos = convertPoint(config["microphone_positions"][3])
source_pos = convertPoint(config["source_position"])
# Source Signal
time = generateTime(meta["sampling_rate"], meta["number_samples"])
source_signal = getSourceSignal(time)
time_window_noise = {"from": 0.01, "to": 0.5}
time_window_signal = {"from": 0.3, "to": 0.4}
signal0 = cutSignal(np.asarray(signals[0]), meta, time_window_signal)
signal1 = cutSignal(np.asarray(signals[1]), meta, time_window_signal)
signal2 = cutSignal(np.asarray(signals[2]), meta, time_window_signal)
signal3 = cutSignal(np.asarray(signals[3]), meta, time_window_signal)
# Do Power Calculations
powerA = getSignalPower_UsingTime_AverageFree(signal0[1])
powerB = getSignalPower_UsingTime_AverageFree(signal1[1])
powerC = getSignalPower_UsingTime_AverageFree(signal2[1])
powerD = getSignalPower_UsingTime_AverageFree(signal3[1])
powerSrc = getSignalPower_UsingTime_AverageFree(source_signal)
print(powerA, "\n", powerB, "\n", powerC, "\n", powerD)
import matplotlib.pyplot as plt
plt.subplot(5, 1, 1)
plt.plot(source_signal)
plt.subplot(5, 1, 2)
plt.plot(signal0[0], signal0[1])
plt.subplot(5, 1, 3)
plt.plot(signal1[0], signal1[1])
plt.subplot(5, 1, 4)
config = load_configs("config.json")[0]
# Starte Simulation
loaded = simulate(config, config["source_position"], signal_function)
signals = loaded.get_measurements()
meta = loaded.get_meta_data()
micA_pos = convertPoint(config["microphone_positions"][0])
micB_pos = convertPoint(config["microphone_positions"][1])
source_pos = convertPoint(config["source_position"])
# Source Signal
time = generateTime(meta["sampling_rate"],meta["number_samples"])
source_signal = getSourceSignal(time)
# Do Power Calculations
powerA = getSignalPower_UsingTime_AverageFree(np.asarray(signals[0]))
powerB = getSignalPower_UsingTime_AverageFree(np.asarray(signals[1]))
powerSrc = getSignalPower_UsingTime_AverageFree(source_signal)
# Calculate TDOA
arr = array_parameters.ArrayParameters(config["microphone_positions"])
tdoa = basic_tdoa.BasicTDOA(loaded, 0, 0.0, arr)
delta_n = tdoa.tdoa_gcc_phat(0.0)[1][0][1][0]
delta_t = delta_n*meta["sampling_spacing"]
delta_s = delta_t*343.2
# TDOA LINEAR VERFAHREN
X_NOL_CURVE, Y_NOL_CURVE = KarstenDOA_calculateCurve_nonlinear(micA_pos, micB_pos, delta_s, res=0.01, rang=10)
X_LIN_CURVE, Y_LIN_CURVE = KarstenDOA_calculateCurve_linear(micA_pos, micB_pos, delta_s, res=0.01, rang=10)
# Plot Data
# Starte Simulation
loaded = simulate(config, config["source_position"], signal_function)
signals = loaded.get_measurements()
meta = loaded.get_meta_data()
micA_pos = convertPoint(config["microphone_positions"][0])
micB_pos = convertPoint(config["microphone_positions"][1])
micC_pos = convertPoint(config["microphone_positions"][2])
micD_pos = convertPoint(config["microphone_positions"][3])
source_pos = convertPoint(config["source_position"])
# Source Signal
time = generateTime(meta["sampling_rate"],meta["number_samples"])
source_signal = getSourceSignal(time)
# Do Power Calculations
powerA = getSignalPower_UsingTime_AverageFree(np.asarray(signals[0]))
powerB = getSignalPower_UsingTime_AverageFree(np.asarray(signals[1]))
powerC = getSignalPower_UsingTime_AverageFree(np.asarray(signals[2]))
powerD = getSignalPower_UsingTime_AverageFree(np.asarray(signals[3]))
powerSrc = getSignalPower_UsingTime_AverageFree(source_signal)
# Calculate TDOA
arr = array_parameters.ArrayParameters(config["microphone_positions"])
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):
# Starte Simulation
loaded = simulate(config)
signals = loaded.get_measurements()
meta = loaded.get_meta_data()
# Source Signal
time = generateTime(meta["sampling_rate"], meta["number_samples"])
source_signal = np.zeros_like(time)
for t in range(0, time.shape[0]):
source_signal[t] = signal_function(time[t])
# Do Power Calculations
powers = list()
for s in signals:
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
# Schneide Zeitfenster auf relevante Bereiche
time_window_noise = {"from": 0.01, "to": 0.05}
time_window_signal = {"from": 0.3, "to": 0.4}
signalsCutNoise = list()
signalsCutSignal = list()
for sig in signals:
signalsCutNoise.append(cutSignal(np.asarray(sig), meta, time_window_noise))
signalsCutSignal.append(
cutSignal(np.asarray(sig), meta, time_window_signal))
# Berechne Signalleistungen
powerNoise = list()
powerSignal = list()
for sig in signalsCutNoise:
powerNoise.append(getSignalPower_UsingTime_AverageFree(sig[1]))
counter = 0
for sig in signalsCutSignal:
powerSignal.append(
getSignalPower_UsingTime_AverageFree(sig[1]) - powerNoise[counter])
counter += 1
# Plot Data
import matplotlib.pyplot as plt
plt.figure(figsize=(20, 10))
# Zeichne Geometrie
plt.subplot(1, 2, 2)
plt.xlim(-10, 10)
plt.ylim(-1, 19)
# Starte Simulation
loaded = simulate(config)
signals = loaded.get_measurements()
meta = loaded.get_meta_data()
# Source Signal
time = generateTime(meta["sampling_rate"], meta["number_samples"])
source_signal = np.zeros_like(time)
for t in range(0, time.shape[0]):
source_signal[t] = signal_function(time[t])
# Do Power Calculations
powers = list()
for s in signals:
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]
