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Tinnitus_Musik_Therapie_Projektarbeit_Repo
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Speichervorgang schneller + IIR-Notch 5ter Ordnung 

Eigentlich müsste jetzt alles so programmiert sein, wie Herr Zwanger es nach dem 11ten Meeting wollte.

IIR Filter 5ter Ordnung und der Speichrvorgang ist wesentlich schneller
master
seyffejn 4 years ago
parent
dda42f6bc4
commit
5278521046
8 changed files with 308 additions and 68 deletions
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  1. 3
    0
      TinnitusAnalyse/.idea/dictionaries/Julian.xml
  2. 191
    2
      TinnitusAnalyse/.idea/workspace.xml
  3. BIN
      TinnitusAnalyse/MyTinnitusFreeSong.wav
  4. 100
    55
      TinnitusAnalyse/SoundGenerator.py
  5. 9
    7
      TinnitusAnalyse/TinnitusAnalyse_GUI.py
  6. 5
    4
      TinnitusAnalyse/TinnitusDaten.csv
  7. BIN
      TinnitusAnalyse/__pycache__/SoundGenerator.cpython-35.pyc
  8. BIN
      TinnitusAnalyse/musikTest.wav

+ 3
- 0
TinnitusAnalyse/.idea/dictionaries/Julian.xml View File

<w>beide</w> <w>beide</w>
<w>beiden</w> <w>beiden</w>
<w>definieren</w> <w>definieren</w>
<w>filterordnung</w>
<w>fontcolor</w> <w>fontcolor</w>
<w>frequenz</w> <w>frequenz</w>
<w>kommentar</w> <w>kommentar</w>
<w>mittelfrequenz</w> <w>mittelfrequenz</w>
<w>nachname</w> <w>nachname</w>
<w>ohren</w> <w>ohren</w>
<w>passband</w>
<w>rauschen</w> <w>rauschen</w>
<w>rauschens</w> <w>rauschens</w>
<w>rechtem</w> <w>rechtem</w>
<w>rechts</w> <w>rechts</w>
<w>speichere</w> <w>speichere</w>
<w>speicherversuch</w> <w>speicherversuch</w>
<w>sperrbereichs</w>
<w>variablen</w> <w>variablen</w>
<w>vorname</w> <w>vorname</w>
<w>zwischen</w> <w>zwischen</w>

+ 191
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TinnitusAnalyse/.idea/workspace.xml View File

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BIN
TinnitusAnalyse/MyTinnitusFreeSong.wav View File


+ 100
- 55
TinnitusAnalyse/SoundGenerator.py View File

import sys # für Fehlermeldungen import sys # für Fehlermeldungen
from scipy import signal from scipy import signal
import csv import csv
#import matplotlib.pyplot as plt
import time
import matplotlib.pyplot as plt

"""--------------------------------------------------------------------------------------------------------------------- """---------------------------------------------------------------------------------------------------------------------
In .wav-Dateien wird der Ton in absoluten Werte eingetragen. Die Standart-framerate ist 44100 In .wav-Dateien wird der Ton in absoluten Werte eingetragen. Die Standart-framerate ist 44100
das heißt für jede Sekunde an Ton gibt es 44100 Werte, die die Tonwelle über die Zeit beschreiben das heißt für jede Sekunde an Ton gibt es 44100 Werte, die die Tonwelle über die Zeit beschreiben
return outdata return outdata


def musik_filtern(self): def musik_filtern(self):
""" Die Tinnitus Frequenz aus der Musik Filtern... easy"""

# Die Parameter des Tinnitus aus .csv Datei getten
"""
Diese Funktion filtert die Tinnitus Frequenz aus einer gewählten Musikdatei. Dabei geht sie in 3 großen
Schritten vor:
1. Die nötigen Informationen über den Tinnitus aus der .csv Datei herausholen
2. Die digitalen Filter erstellen und die Tinnitus Frequenz aus der Audiodatei "herausschneiden"
3. Die fertigen Audiodatei als .wav Datei speichern
"""

# ------------1. Die nötigen Informationen über den Tinnitus aus der .csv Datei herausholen---------------------
csvstring = open("TinnitusDaten.csv").read() csvstring = open("TinnitusDaten.csv").read()
#print(csvstring)
x = csvstring.split("\n")
#print("\nx= ", x)
tinnitus_data = csvstring.split("\n")


# linke Frequenz aus csv Datei holen # linke Frequenz aus csv Datei holen
lf = x[2]
lf = tinnitus_data[2]
lf = lf.split(";") lf = lf.split(";")
lf = float(lf[1]) lf = float(lf[1])




# rechte Frequenz aus csv Datei holen # rechte Frequenz aus csv Datei holen
rf = x[7]
rf = tinnitus_data[7]
rf = rf.split(";") rf = rf.split(";")
rf = float(rf[1]) rf = float(rf[1])




# linke Lautstärke aus cvs Datei holen # linke Lautstärke aus cvs Datei holen
ll = x[3]
ll = tinnitus_data[3]
ll = ll.split(";") ll = ll.split(";")
ll = float(ll[1]) ll = float(ll[1])


# rechte Lautstärke aus cvs Datei holen # rechte Lautstärke aus cvs Datei holen
rl = x[8]
rl = tinnitus_data[8]
rl = rl.split(";") rl = rl.split(";")
rl = float(rl[1]) rl = float(rl[1])



# Die Musik filtern
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.
# -------- 2. Die digitalen Filter erstellen und die Tinnitus Frequenz aus der Audiodatei "herausschneiden------
start_time = time.time() # einen Timer laufen lassen um zu sehen wie lange Filterung dauert


self.music_data = self.music_data/32767 * 0.8 # convert array from int16 to float self.music_data = self.music_data/32767 * 0.8 # 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) b, a = signal.iirnotch(lf, Q, fs=self.music_samplerate)
if ll != 0.0:
print("Links wird gefiltert...")
musicLinks = signal.lfilter(b, a , self.music_data[:, 0]) # links
print("Filtern beendet")
else:
musicLinks = self.music_data[:, 0] # ungefiltert, wenn kein Tinnitus angegeben wurde

#Plot
# 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([-25, 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()

b, a = signal.iirnotch(rf, Q, fs=self.music_samplerate)
if rl != 0.0:
print("Rechts wird gefiltert...")
musicRechts = signal.lfilter(b, a, self.music_data[:, 1]) # rechts
print("Filtern beendet")
----------------------------------------------------------------------------------------------------------------
"""

""" New IIR Notch Filter 5th order--------------------------------------------------------------------------"""
# ------------------------------------------LEFT EAR FILTERING-------------------------------------------------
# Filterparameter festlegen------------
order = 5 # Filterordnung
bandwidth = 175 # 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)
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 filtert die Audiodaten
else: else:
musicRechts = self.music_data[:, 1] # ungefiltert, wenn kein Tinnitus angegeben wurde
music_links = self.music_data[:, 0] # ungefiltert, wenn kein Tinnitus angegeben wurde


# ------------------------------------- RIGHT EAR FILTERING ------------------------------------------------
if rl != 0.0: # nur wenn die Lautstärke des rechten Tinnitus ungleich 0 ist, wird auf diesem Ohr auch gefiltert
cutoff_frequencies = [(rf - (bandwidth / 2)), (
rf + (bandwidth / 2))] # change the cutoff frequencies to the tinnitus of the RIGHT EAR


wav_obj = wave.open("musikTest.wav", "w")
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
else:
music_rechts = self.music_data[:, 1] # diese Funktion filtert die Audiodaten(die Tinnitusfreq wird entfernt)

endTimeFiltering = time.time()
print("benötigte Zeit zum Filtern rechts Ohr =", endTimeFiltering - start_time, "s")

# ------------------------- 3. Die fertigen Audiodatei als .wav Datei speichern --------------------------------
start_time = time.time()
wav_obj = wave.open("MyTinnitusFreeSong.wav", "w")
# Rahmenparameter für die .wav-Datei setzen # Rahmenparameter für die .wav-Datei setzen
nframes = len(self.music_data) #Gesamtanzahl der Frames in der Musikdatei nframes = len(self.music_data) #Gesamtanzahl der Frames in der Musikdatei
wav_obj.setparams((self.nchannels, self.sampwidth, self.music_samplerate, nframes, self.comptype, self.compname)) wav_obj.setparams((self.nchannels, self.sampwidth, self.music_samplerate, nframes, self.comptype, self.compname))


print("Maximum musicLinks: ", max(musicLinks))
print("Minimum musikLinks: ", min(musicLinks))
"""The values are stored in a temporary list, and when the process is finished, they are joined together into
an string which is then sent to the output with the traditional writeframes method."""


packedMusic = [] # Liste an die wir die einzelnen frames bereits in binär umgewandelt aneinanderreihen


# Die Audiosamples schreiben # Die Audiosamples schreiben
print("Musikdatei wird erstellt...") print("Musikdatei wird erstellt...")
for x in range(self.music_samplerate * 20): #Kann mit nframes ersetzt werden, für den ganzen Song
for tinnitus_data in range(self.music_samplerate * 60): #Kann mit nframes ersetzt werden, für den ganzen Song
# Die Audiodaten müssen von float in einen passenden int-Wert umgerechnet werden # Die Audiodaten müssen von float in einen passenden int-Wert umgerechnet werden
wav_obj.writeframes(struct.pack('h', int(musicLinks[x] * 32767.0))) # Werte für links und rechts werden bei
wav_obj.writeframes(struct.pack('h', int(musicRechts[x] * 32767.0))) # wav abwechselnd eingetragen
if x % 10000 == 0:
fortschritt = x/(self.music_samplerate*20)*100
packedMusic.append(struct.pack('h', int(music_links[tinnitus_data] * 32767.0)))
packedMusic.append(struct.pack('h', int(music_rechts[tinnitus_data] * 32767.0)))

# wav_obj.writeframes(struct.pack('h', int(music_links[x] * 32767.0))) # Werte für links und rechts werden bei
# wav_obj.writeframes(struct.pack('h', int(musicRechts[x] * 32767.0))) # wav abwechselnd eingetragen
if tinnitus_data % 10000 == 0:
fortschritt = tinnitus_data/(self.music_samplerate*20)*100
print(" ", round(fortschritt, 1), "%") print(" ", round(fortschritt, 1), "%")


end_time = time.time()
print("Zeitaufwand für das packen der einzelnen Samples =", end_time - start_time, "s")


value_str = b"".join(packedMusic)

start = time.time()
wav_obj.writeframes(value_str)
end = time.time()
print("Zeitaufwand für das schreiben aller Frames in die wav Datei =", end - start, "s")
wav_obj.close() wav_obj.close()

print("Speichern beendet.") print("Speichern beendet.")
# # 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()

+ 9
- 7
TinnitusAnalyse/TinnitusAnalyse_GUI.py View File

from scipy.io import wavfile from scipy.io import wavfile







"""--------------------------FUNKTIONEN DIE DURCH GUI KLICKS AUSGEFÜHRT WERDEN---------------------------------------""" """--------------------------FUNKTIONEN DIE DURCH GUI KLICKS AUSGEFÜHRT WERDEN---------------------------------------"""
"""-------Funktionen links-------------""" """-------Funktionen links-------------"""




def unten_button_filtere_tinnitus_aus_musik(): def unten_button_filtere_tinnitus_aus_musik():
print("button filtere tinnitus aus musik pressed") print("button filtere tinnitus aus musik pressed")
feedback("Gefilterte Musikdatei erstellt", "blue")
sound.musik_filtern()
# except:
# feedback("Fehlgeschlagener Filterversuch!", "red", "white")
try:
feedback("Starte Filtervorgang (dies kann einige Minuten dauern)...", "blue")
sound.musik_filtern()
feedback("Filtervorgang erfolgreich abgeschlossen. \n"
"Audiodatei unter dem Namen MyTinnitusFreeSong.wav erstellt", "white", "green")
except:
feedback("Fehlgeschlagener Filterversuch. Drücke zuerst den Speichern Knopf"
"Stelle sicher, dass die Lautstärke mindestens einer Seite über 0"
"gestellt ist. Sonst gehen wir davon aus, dass auf diesem Ohr kein Tinnitus vorliegt.", "red", "white")




""" Initialisierungen """ """ Initialisierungen """

+ 5
- 4
TinnitusAnalyse/TinnitusDaten.csv View File

Vorname;asd Vorname;asd
Nachname;asd Nachname;asd
linke Frequenz;10000.0
linke Lautstärke;0.2
linke Frequenz;5040.0
linke Lautstärke;0.04
linkes Rauschen Lautstärke;0.0 linkes Rauschen Lautstärke;0.0
linkes Rauschen untere Grenzfrequenz;10.0 linkes Rauschen untere Grenzfrequenz;10.0
linkes Rauschen obere Grenzfrequenz;20000.0 linkes Rauschen obere Grenzfrequenz;20000.0
rechte Frequenz;920.0
rechte Lautstärke;0.28
rechte Frequenz;5040.0
rechte Lautstärke;0.02
rechtes Rauschen Lautstärke;0.0 rechtes Rauschen Lautstärke;0.0
rechtes Rauschen untere Grenzfrequenz;10.0 rechtes Rauschen untere Grenzfrequenz;10.0
rechtes Rauschen obere Grenzfrequenz;20000.0 rechtes Rauschen obere Grenzfrequenz;20000.0
Kommentar; Kommentar;


BIN
TinnitusAnalyse/__pycache__/SoundGenerator.cpython-35.pyc View File


BIN
TinnitusAnalyse/musikTest.wav View File


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