Update NEU: (DigitalFilterTest.py)

DigitalFilterTest.py hinzugefügt.
Dieses Skript beinhaltet die Differenzengleichung eines Bandpass nach Tobola MEM_C Vorlesung. Außerdem plottet es schnell die Frequenzen um zu testen ob der Filter funktioniert.

TinnitusAnalyse/.idea/workspace.xml

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TinnitusAnalyse/DigitalFilterTest.py

+import matplotlib.pyplot as plt  # For plotting
+from math import sin, pi, cos  # For generating input signals
+import numpy as np
+import sys  # For reading command line arguments
+
+
+fs = 44100  # sampling frequency (Abtastfrequenz)
+
+koeff = {
+    "b0": 0,
+    "b1": 0,
+    "b2": 0,
+    "a1": 0,
+    "a2": 0
+}
+
+
+def koeffizienten_berechnen(omega, r):
+    # Koeffizientenberechnung nach Tobola VL S.107 - IIR Filter, 2. Ordnung
+    koeff["b0"] = 1
+    koeff["b1"] = 0
+    koeff["b2"] = -1
+    koeff["a1"] = -2*r*cos(omega)
+    koeff["a2"] = r**2
+
+
+def filter(x):
+    y = [0]*len(x)
+    for k in range(4, len(x)):
+        y[k] = koeff["b0"]*x[k] + koeff["b1"]*x[k-1] + koeff["b2"]*x[k-2] - koeff["a1"]*y[k-1] - koeff["a2"]*y[k-2]
+
+    return y
+
+
+
+
+dauer_ms = 10000  # 10 Sekunden
+num_samples = dauer_ms * (fs / 1000)  # framerate -pro Sekunde- umgerechnet in -pro Millisekunde-
+
+t = np.linspace(0, 10, int(num_samples)) # array zum darstellen der x-Achse
+amp = 1
+
+f_input1 = 1
+f_input2 = 50
+f_input3 = 10
+
+input1 = []
+input2 = []
+input3 = []
+for x in range(int(num_samples)):  # einen einfachen Sinus ins array schreiben
+    input1.append(amp * sin(2 * pi * f_input1 * (x / fs)))
+    input2.append(amp * sin(2 * pi * f_input2 * (x / fs)))
+    #input3.append(amp * sin(2 * pi * f_input3 * (x / fs)))
+
+
+input_ges = np.add(input1, input2) # Sinus aufaddieren um ein halbwegs realistisches Audiosignal zu bekommen
+#input_ges = np.add(input_ges, input3)
+
+#Filterparameter hier einstellen
+fr = 5
+omega = 2*pi*fr/fs
+r = 0.95
+koeffizienten_berechnen(omega, r)   # Koeffizienten berechnen mit Mittelfrequenz 10Hz
+
+output = filter(input_ges)
+
+### Plot the signals for comparison
+plt.figure(1)
+plt.subplot(231)
+plt.ylabel('Amplitude')
+plt.xlabel('t [s]')
+plt.title('f_1=' + str(f_input1) + "Hz")
+plt.plot(t, input1)
+
+plt.subplot(232)
+plt.ylabel('Amplitude')
+plt.xlabel('t [s]')
+plt.title('f_2=' + str(f_input2) + "Hz")
+plt.plot(t, input2)
+
+# plt.subplot(233)
+# plt.ylabel('Amplitude')
+# plt.xlabel('t [s]')
+# plt.title('f_3=' + str(f_input3) + "Hz")
+# plt.plot(t, input3)
+
+plt.subplot(234)
+plt.ylabel('Amplitude')
+plt.xlabel('t [s]')
+plt.title('(input in Filter) f_ges = f_1 + f_2')
+plt.plot(t, input_ges)
+
+plt.subplot(235)
+plt.ylabel('Amplitude')
+plt.xlabel('Samples')
+plt.title('gefiltertes Signal, mit f_r = ' + str(fr) + "Hz und r=" + str(r))
+plt.plot(t, output)
+plt.show()

TinnitusAnalyse/SoundGenerator.py

         # zuerst muss ein Array mit Audiodaten gefüllt werden
         audio = []
         freq = self.tinnitus.linksFrequenz
-        dauer_ms = 1000.0  # 10 Sekunden
+        dauer_ms = 5000.0  # 10 Sekunden
         amp = self.tinnitus.linksLautstaerke
         rauschen = self.tinnitus.linksRauschenLautstaerke
 

TinnitusAnalyse/TinnitusDaten.csv

-Vorname;asd
-Nachname;asda
-linke Frequenz;770.0
-linke Lautstärke;0.01
+Vorname;asdas
+Nachname;asdas
+linke Frequenz;0.0
+linke Lautstärke;0.0
 linkes Rauschen;0.0
-rechte Frequenz;1410.0
-rechte Lautstärke;0.03
+rechte Frequenz;0.0
+rechte Lautstärke;0.0
 rechtes Rauschen;0.0
-Kommentar;asd
+Kommentar;asdas