FIR Filter implementierrt

TinnitusAnalyse/.idea/workspace.xml

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TinnitusAnalyse/SoundGenerator.py

         rl = float(rl[1])
 
         # -------- 2. Die digitalen Filter erstellen und die Tinnitus Frequenz aus der Audiodatei "herausschneiden------
-        self.filterfortschritt = 2, 0  # der zweite schritt
+        self.filterfortschritt = 2, 0  # der zweite schritt im Feedback
         start_time = time.time()    # einen Timer laufen lassen um zu sehen wie lange Filterung dauert
 
-        self.music_data = self.music_data/32767 # convert array from int16 to float
-        """ OLD IIR Notch Filter 2nd Order----------------------------------------------------------------------
-        w0 = float(lf / (self.music_samplerate / 2))  # Frequency to remove from a signal. If fs is specified, this is 
-        in the same units as fs. By default, it is a normalized scalar that must satisfy 0 < w0 < 1, with w0 = 1 
-        corresponding to half of the sampling frequency.
-        Q = 30.0  # Quality factor. Dimensionless parameter that characterizes notch filter -3 dB bandwidth bw relative 
-        to its center frequency, Q = w0/bw.
-        b, a = signal.iirnotch(lf, Q, fs=self.music_samplerate)
-        ----------------------------------------------------------------------------------------------------------------
-        """
+        self.music_data = self.music_data/32767  # convert array from int16 to float
 
-        """ New IIR Notch Filter 5th order--------------------------------------------------------------------------"""
         # ------------------------------------------LEFT EAR FILTERING-------------------------------------------------
         # Filterparameter festlegen------------
-        order = 5   # Filterordnung
-        bandwidth = 175     # Bandbreite des Sperrbereichs in Hz
+        order = 501   # Filterordnung
+        bandwidth = 1000     # Bandbreite des Sperrbereichs in Hz
         #stop_attenuation = 100  # minimum Attenuation (Damping, Reduction) in stop Band [only for elliptic filter necessary]
-        cutoff_frequencies = [(lf - (bandwidth / 2)),(lf + (bandwidth / 2))]  # the cutoff frequencies (lower and upper)
+        cutoff_frequencies = [(lf - (bandwidth / 2)), (lf + (bandwidth / 2))]  # the cutoff frequencies (lower and upper)
         max_ripple_passband = 50    # Maximal erlaubte Welligkeit im Passbereich
         # -------------------------------------
 
         if ll != 0.0:   # nur wenn die Lautstärke des linken Tinnitus ungleich 0 ist, wird auf diesem Ohr auch gefiltert
-            b, a = signal.iirfilter(order, cutoff_frequencies, rp=max_ripple_passband, btype='bandstop', ftype='butter',
-                                    fs=self.music_samplerate)  # Diese Funktion erstellt den IIR-Bandpassfilter (links)
-
-            music_links = signal.lfilter(b, a, self.music_data[:, 0])  # diese Funktion wendet den Filter an
-
-            # FIR Filterversuch
-            # b = signal.firwin(order, cutoff_frequencies, pass_zero="bandstop", fs=self.music_samplerate, width=bandwidth,
-            #                   window="hamming")
+            # b, a = signal.iirfilter(order, cutoff_frequencies, rp=max_ripple_passband, btype='bandstop', ftype='butter',
+            #                         fs=self.music_samplerate)  # Diese Funktion erstellt den IIR-Bandpassfilter (links)
+            #
+            # music_links = signal.lfilter(b, a, self.music_data[:, 0])  # diese Funktion wendet den Filter an
             #
-            # music_links = signal.lfilter(b, [1.0], self.music_data[:, 0])
+            # print("b=", b)
+            # print("a=", a)
 
+            # FIR Filterversuch
+            #h = signal.firwin(order, cutoff_frequencies, pass_zero="bandstop", fs=self.music_samplerate, width=bandwidth,
+            #                  window="hamming")
+            h = signal.firwin(order, [cutoff_frequencies[0], cutoff_frequencies[1]], fs=self.music_samplerate)
+            print("h= ", h)
+            music_links = signal.lfilter(h, 1.0, self.music_data[:, 0])
 
         else:
             music_links = self.music_data[:, 0]  # ungefiltert, wenn kein Tinnitus angegeben wurde
             cutoff_frequencies = [(rf - (bandwidth / 2)), (
                         rf + (bandwidth / 2))]  # change the cutoff frequencies to the tinnitus of the RIGHT EAR
 
-            b, a = signal.iirfilter(order, cutoff_frequencies, rp=max_ripple_passband, btype='bandstop', ftype='butter',
-                                    fs=self.music_samplerate)  # Diese Funktion erstellt den IIR-Bandpassfilter (rechts)
-
-            music_rechts = signal.lfilter(b, a, self.music_data[:, 1])    # rechts
+            # h = signal.iirfilter(order, cutoff_frequencies, rp=max_ripple_passband, btype='bandstop', ftype='butter',
+            #                         fs=self.music_samplerate)  # Diese Funktion erstellt den IIR-Bandpassfilter (rechts)
+            #
+            # music_rechts = signal.lfilter(b, a, self.music_data[:, 1])    # rechts
 
             # FIR Filterversuch
-            # b = signal.firwin(order, cutoff_frequencies, pass_zero="bandstop", fs=self.music_samplerate, width=bandwidth,
-            #                   window="hamming")
-            #
-            # music_rechts = signal.lfilter(b, [1.0], self.music_data[:, 1])
+            print("UG Freq = ", cutoff_frequencies[0]/(self.music_samplerate/2))
+            h = signal.firwin(order, [cutoff_frequencies[0], cutoff_frequencies[1]], fs=self.music_samplerate)
+
+            music_rechts = signal.lfilter(h, [1.0], self.music_data[:, 1])
         else:
             music_rechts = self.music_data[:, 1]  # diese Funktion filtert die Audiodaten(die Tinnitusfreq wird entfernt)
 
                 # self.filterfortschritt = 3, round(fortschritt, 1)
                 # print("  max: ", self.filterfortschritt[1], "%")
 
-                fortschritt += 10
                 self.filterfortschritt = 3, round(fortschritt, 1)
                 print("  max: ", self.filterfortschritt[1], "%")
+                fortschritt += 10
         end_time = time.time()
         print("Zeitaufwand Maxima-Suche: ", end_time - start_time)
 
         print("Speichern beendet.")
         self.filterfortschritt = 5, 0  #Nach erfolgreichem Filtern Fortschritt zur Bestätigung auf 5 setzen
         # Plot (hilfreich für Filterentwurf)
-        freq, h = signal.freqz(b, a, fs=self.music_samplerate)
-        fig, ax = plt.subplots(2, 1, figsize=(8, 6))
-        ax[0].plot(freq, 20 * np.log10(abs(h)), color='blue')
-        ax[0].set_title("Frequency Response")
-        ax[0].set_ylabel("Amplitude (dB)", color='blue')
-        ax[0].set_xlim([0, 10000])
-        ax[0].set_ylim([-120, 10])
-        ax[0].grid()
-        ax[1].plot(freq, np.unwrap(np.angle(h)) * 180 / np.pi, color='green')
-        ax[1].set_ylabel("Angle (degrees)", color='green')
-        ax[1].set_xlabel("Frequency (Hz)")
-        ax[1].set_xlim([0, 10000])
-        ax[1].set_yticks([-90, -60, -30, 0, 30, 60, 90])
-        ax[1].set_ylim([-90, 90])
-        ax[1].grid()
-        plt.show()
+        # freq, h = signal.freqz(b, a, fs=self.music_samplerate)
+        # fig, ax = plt.subplots(2, 1, figsize=(8, 6))
+        # ax[0].plot(freq, 20 * np.log10(abs(h)), color='blue')
+        # ax[0].set_title("Frequency Response")
+        # ax[0].set_ylabel("Amplitude (dB)", color='blue')
+        # ax[0].set_xlim([0, 10000])
+        # ax[0].set_ylim([-120, 10])
+        # ax[0].grid()
+        # ax[1].plot(freq, np.unwrap(np.angle(h)) * 180 / np.pi, color='green')
+        # ax[1].set_ylabel("Angle (degrees)", color='green')
+        # ax[1].set_xlabel("Frequency (Hz)")
+        # ax[1].set_xlim([0, 10000])
+        # ax[1].set_yticks([-90, -60, -30, 0, 30, 60, 90])
+        # ax[1].set_ylim([-90, 90])
+        # ax[1].grid()
+        # plt.show()