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FIR Filter implementierrt

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seyffejn 2020-10-03 20:18:47 +02:00
parent aa13d8128f
commit 0894325737
6 changed files with 81 additions and 73 deletions
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TinnitusAnalyse/.idea/workspace.xml generated
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<find>Daten er</find> <find>Daten er</find>
<find>samples</find> <find>samples</find>
<find>feedback</find>
<find>framerate</find> <find>framerate</find>
<find>sample</find>
<find>samplerate</find>
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<find>feedback</find>
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TinnitusAnalyse/SoundGenerator.py
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@ -232,42 +232,35 @@ class Sound:
rl = float(rl[1]) rl = float(rl[1])
# -------- 2. Die digitalen Filter erstellen und die Tinnitus Frequenz aus der Audiodatei "herausschneiden------ # -------- 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 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 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)
----------------------------------------------------------------------------------------------------------------
"""
""" New IIR Notch Filter 5th order--------------------------------------------------------------------------"""
# ------------------------------------------LEFT EAR FILTERING------------------------------------------------- # ------------------------------------------LEFT EAR FILTERING-------------------------------------------------
# Filterparameter festlegen------------ # Filterparameter festlegen------------
order = 5 # Filterordnung order = 501 # Filterordnung
bandwidth = 175 # Bandbreite des Sperrbereichs in Hz bandwidth = 1000 # Bandbreite des Sperrbereichs in Hz
#stop_attenuation = 100 # minimum Attenuation (Damping, Reduction) in stop Band [only for elliptic filter necessary] #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 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 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', # 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) # 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, a, self.music_data[:, 0]) # diese Funktion wendet den Filter an
#
# print("b=", b)
# print("a=", a)
# FIR Filterversuch # FIR Filterversuch
# b = signal.firwin(order, cutoff_frequencies, pass_zero="bandstop", fs=self.music_samplerate, width=bandwidth, #h = signal.firwin(order, cutoff_frequencies, pass_zero="bandstop", fs=self.music_samplerate, width=bandwidth,
# window="hamming") # window="hamming")
# h = signal.firwin(order, [cutoff_frequencies[0], cutoff_frequencies[1]], fs=self.music_samplerate)
# music_links = signal.lfilter(b, [1.0], self.music_data[:, 0]) print("h= ", h)
music_links = signal.lfilter(h, 1.0, self.music_data[:, 0])
else: else:
music_links = self.music_data[:, 0] # ungefiltert, wenn kein Tinnitus angegeben wurde music_links = self.music_data[:, 0] # ungefiltert, wenn kein Tinnitus angegeben wurde
@ -277,16 +270,16 @@ class Sound:
cutoff_frequencies = [(rf - (bandwidth / 2)), ( cutoff_frequencies = [(rf - (bandwidth / 2)), (
rf + (bandwidth / 2))] # change the cutoff frequencies to the tinnitus of the RIGHT EAR 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', # 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) # fs=self.music_samplerate) # Diese Funktion erstellt den IIR-Bandpassfilter (rechts)
#
music_rechts = signal.lfilter(b, a, self.music_data[:, 1]) # rechts # music_rechts = signal.lfilter(b, a, self.music_data[:, 1]) # rechts
# FIR Filterversuch # FIR Filterversuch
# b = signal.firwin(order, cutoff_frequencies, pass_zero="bandstop", fs=self.music_samplerate, width=bandwidth, print("UG Freq = ", cutoff_frequencies[0]/(self.music_samplerate/2))
# window="hamming") h = signal.firwin(order, [cutoff_frequencies[0], cutoff_frequencies[1]], fs=self.music_samplerate)
#
# music_rechts = signal.lfilter(b, [1.0], self.music_data[:, 1]) music_rechts = signal.lfilter(h, [1.0], self.music_data[:, 1])
else: else:
music_rechts = self.music_data[:, 1] # diese Funktion filtert die Audiodaten(die Tinnitusfreq wird entfernt) music_rechts = self.music_data[:, 1] # diese Funktion filtert die Audiodaten(die Tinnitusfreq wird entfernt)
@ -310,9 +303,9 @@ class Sound:
# self.filterfortschritt = 3, round(fortschritt, 1) # self.filterfortschritt = 3, round(fortschritt, 1)
# print(" max: ", self.filterfortschritt[1], "%") # print(" max: ", self.filterfortschritt[1], "%")
fortschritt += 10
self.filterfortschritt = 3, round(fortschritt, 1) self.filterfortschritt = 3, round(fortschritt, 1)
print(" max: ", self.filterfortschritt[1], "%") print(" max: ", self.filterfortschritt[1], "%")
fortschritt += 10
end_time = time.time() end_time = time.time()
print("Zeitaufwand Maxima-Suche: ", end_time - start_time) print("Zeitaufwand Maxima-Suche: ", end_time - start_time)
@ -375,19 +368,19 @@ class Sound:
print("Speichern beendet.") print("Speichern beendet.")
self.filterfortschritt = 5, 0 #Nach erfolgreichem Filtern Fortschritt zur Bestätigung auf 5 setzen self.filterfortschritt = 5, 0 #Nach erfolgreichem Filtern Fortschritt zur Bestätigung auf 5 setzen
# Plot (hilfreich für Filterentwurf) # Plot (hilfreich für Filterentwurf)
freq, h = signal.freqz(b, a, fs=self.music_samplerate) # freq, h = signal.freqz(b, a, fs=self.music_samplerate)
fig, ax = plt.subplots(2, 1, figsize=(8, 6)) # fig, ax = plt.subplots(2, 1, figsize=(8, 6))
ax[0].plot(freq, 20 * np.log10(abs(h)), color='blue') # ax[0].plot(freq, 20 * np.log10(abs(h)), color='blue')
ax[0].set_title("Frequency Response") # ax[0].set_title("Frequency Response")
ax[0].set_ylabel("Amplitude (dB)", color='blue') # ax[0].set_ylabel("Amplitude (dB)", color='blue')
ax[0].set_xlim([0, 10000]) # ax[0].set_xlim([0, 10000])
ax[0].set_ylim([-120, 10]) # ax[0].set_ylim([-120, 10])
ax[0].grid() # ax[0].grid()
ax[1].plot(freq, np.unwrap(np.angle(h)) * 180 / np.pi, color='green') # 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_ylabel("Angle (degrees)", color='green')
ax[1].set_xlabel("Frequency (Hz)") # ax[1].set_xlabel("Frequency (Hz)")
ax[1].set_xlim([0, 10000]) # ax[1].set_xlim([0, 10000])
ax[1].set_yticks([-90, -60, -30, 0, 30, 60, 90]) # ax[1].set_yticks([-90, -60, -30, 0, 30, 60, 90])
ax[1].set_ylim([-90, 90]) # ax[1].set_ylim([-90, 90])
ax[1].grid() # ax[1].grid()
plt.show() # plt.show()

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