Initial commit

fcts.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Thu Mar 25 10:04:06 2021
+
+@author: lucasdaniele
+"""
+
+from scipy.io import wavfile
+import matplotlib.pyplot as plt
+import numpy as np
+import frequency_estimator.frequency_estimator as f0
+
+
+def listen(nom,sampleRate, data):
+    wavfile.write(nom, sampleRate, data.real)
+    
+def aff(titre,x,y):
+    if(len(x)==len(y)):
+        plt.plot(x, y.real, "-")
+    else:
+        plt.plot(y.real, "-")
+    plt.title(titre)
+    plt.show();
+    
+def decouperExtraits(data):
+    DATA=[]
+    cut=[]
+    onAir=False
+    step=10000
+    k=0
+    seuil=1500
+    for i in range(step,len(data)):
+        if ((abs(data[i])<seuil) & onAir):
+            if(k<step):
+                k+=1
+                cut.append(data[i])
+            else:
+                k=0
+                onAir=False
+                DATA.append(cut)
+                cut=[]
+        else:
+            if(abs(data[i])>=seuil):
+                if(not(onAir)):
+                    onAir=True
+                k=0
+                cut.append(data[i])
+    DATA.append(cut)
+    return DATA    
+      
+        
+def garderUniquementCris(sampleRate,tMin, tMax, fMin, fMax, f0Func, dataCut):
+    nSamplesMin = tMin*sampleRate 
+    nSamplesMax = tMax*sampleRate
+    res=[]
+    for i in range(len(dataCut)):
+        if((len(dataCut[i])>nSamplesMin) & (len(dataCut[i])<nSamplesMax)):
+            freq = np.round(f0Func(np.asarray(dataCut[i]),sampleRate))
+            if((freq>fMin) & (freq<fMax)):
+                res.append(dataCut[i])
+    return res
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frequency_estimator @ f94aa1d3

+Subproject commit f94aa1d3d63a8ec3c0e80f30c6f9f4e375ceea0f

test1.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Wed Mar 24 21:25:34 2021
+
+@author: lucasdaniele
+"""
+
+from scipy.io import wavfile
+import numpy as np
+import matplotlib.pyplot as plt
+import fcts as fcts
+
+
+################################################
+# Extraction des données
+################################################
+#bEnSec = (4*60+43)
+#bEnSec = (5*60+16)
+bEnSec = (5*60+43.8)
+duree = 0.7
+
+
+samplerate, dataLR = wavfile.read("test1.wav")#On lit le fichier qui a des infos oreille gauche, oreille droite
+dataL = dataLR[:,0]#On recupère que le son de l'oreille gauche
+T = 1/samplerate;
+b = int(round(bEnSec*samplerate))
+n = int(round(duree*samplerate));
+t = np.linspace(bEnSec, bEnSec+n*T, n)
+data = dataL[b:n+b]#On recupere n echantillons depuis b soit depuis bEnSec --> Support temporel
+
+
+################################################
+# Support temporel sans modifs
+################################################
+
+fcts.aff("Support temporel sans modifs",t,data)
+fcts.listen("extrait.wav", samplerate, data)
+
+################################################
+# Support temporel sans silences
+################################################
+
+dataSansSilence = fcts.supprmierSilences(1500, data)
+fcts.aff("Support temporel sans les silences",t,dataSansSilence)
+fcts.listen("extraitSansSilence.wav", samplerate, dataSansSilence)
+
+################################################
+# Support frequentiel sans modifs
+################################################
+
+f,dataf = fcts.timeToFrequency(t, dataSansSilence)
+# fcts.aff("Support frequentiel sans modifs",f,abs(dataf))
+
+################################################
+# Support frequentiel sans parasites
+################################################
+
+dataf = fcts.passeBas(1300,0,f,dataf)
+# fcts.aff("Support frequentiel avec passe bas", f, abs(dataf))
+
+################################################
+# Support temporel sans parasites
+################################################
+
+dataSansBruit = fcts.frequencyToTime(f, dataf)
+fcts.aff("Support temporel sans les parasites",t,dataSansBruit)
+fcts.listen("extraitSansParasite.wav", samplerate, dataSansBruit)
+
+
+# # ################################################
+# # # Support frequentiel avec filtre anti balle
+# # ################################################
+
+# dataf = fcts.coupeBande(205,5,0,f,dataf)
+# dataSansBalle = fcts.frequencyToTime(f, dataf)
+
+
+
+

test2.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Mar 26 09:45:25 2021
+
+@author: lucasdaniele
+"""
+
+from scipy.io import wavfile
+import numpy as np
+import matplotlib.pyplot as plt
+import fcts as fcts
+import frequency_estimator.frequency_estimator as f0
+
+
+
+################################################
+# Extraction des données
+################################################
+
+#bEnSec = (4*60+43)
+#bEnSec = (5*60+16)
+bEnSec = (3600+21*60+34)
+duree = 300
+
+
+samplerate, dataLR = wavfile.read("input/male.wav")#On lit le fichier qui a des infos oreille gauche, oreille droite
+dataL = dataLR[:,0]#On recupère que le son de l'oreille gauche
+T = 1/samplerate;
+b = int(round(bEnSec*samplerate))
+n = int(round(duree*samplerate));
+t = np.linspace(bEnSec, bEnSec+n*T, n)
+data = dataL[b:n+b]#On recupere n echantillons depuis b soit depuis bEnSec --> Support temporel
+
+################################################
+# Support temporel sans modifs
+################################################
+
+fcts.aff("Support temporel sans modifs",t,data)
+fcts.listen("output/extrait.wav", samplerate, data)
+
+################################################
+# Support temporel signal découpé
+################################################
+
+dataCut = fcts.decouperExtraits(data)
+
+################################################
+# Support temporel uniquement cris
+################################################
+
+dataCutCris = fcts.garderUniquementCris(samplerate,0.3,1,900,1000,f0.freq_from_crossings, dataCut)
+for i in range(len(dataCutCris)):   
+    fcts.aff("cri"+str(i), t, np.asarray(dataCutCris[i]))
+    fcts.listen("output/cri"+str(i)+".wav", samplerate, np.asarray(dataCutCris[i]))
+    print("enregistrement: " + str(i))
+    print(np.round(f0.freq_from_crossings(np.asarray(dataCutCris[i]),samplerate)))
+    print("________________")
+
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test3.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Mar 26 15:58:02 2021
+
+@author: lucasdaniele
+"""
+import fcts as fcts
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.io import wavfile
+import scipy.signal
+
+bEnSec = (5*60+43)
+duree = 5
+
+fe, dataLR = wavfile.read("test1.wav")#On lit le fichier qui a des infos oreille gauche, oreille droite
+dataL = dataLR[:,0]#On recupère que le son de l'oreille gauche
+te = 1/fe;
+b = int(round(bEnSec*fe))
+n = int(round(duree*fe));
+t = np.linspace(bEnSec, bEnSec+n*te, n)
+x = dataL[b:n+b]#On recupere n echantillons depuis b soit depuis bEnSec --> Support temporel
+
+
+plt.plot(t,x);
+fcts.listen("extrait.wav", fe, np.asarray(x))
+plt.show();
+
+
+fca = 200 # frequence de coupure basse
+fcb = 210 # coupure haute
+a = fca/fe
+b = fcb/fe
+P = 50
+h = fcts.filtreRIF("CBande",a,b,P,"hann")
+[f,g,phi] = fcts.reponseFreq(h)
+y = scipy.signal.convolve(x,h,mode='valid')
+ny = y.size
+ty = np.zeros(ny)
+for k in range(ny):
+    ty[k] = P*te+te*k
+    
+plt.plot(ty,y);
+fcts.listen("extraitFiltre.wav", fe,  np.asarray(y))
+plt.show();
\ No newline at end of file

test4corr.py

+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Mar 28 16:42:37 2021
+
+@author: lucasdaniele
+"""
+
+from scipy.io import wavfile
+import numpy as np
+
+
+samplerate, d1 = wavfile.read("cri.wav")
+superD1 = np.zeros(int(np.ceil(len(d1)/10)))
+for i in range(len(d1)):
+    if(i%3==0):
+        superD1[int(np.floor(i/10))]=d1[i]
+        
+samplerate, d0 = wavfile.read("extrait.wav")
+superD0 = np.zeros(int(np.ceil(len(d0)/10)))
+for i in range(len(d0)):
+    if(i%3==0):
+        superD0[int(np.floor(i/10))]=d0[i]
+        
+T = 1/samplerate;
+max_=0
+for i in range(0,len(superD0)-len(superD1),1):
+    corr = np.corrcoef(superD0[i:i+len(superD1)],superD1)[0,1]
+    if (corr>max_):
+        max_ = corr
+        print(corr, i)
+print(max_)