Ajout d'un tri sur la forme du cri

cells.py

@@ -13,8 +13,8 @@ import numpy as np
 import fcts as fcts
 import importlib
 import frequency_estimator as f0
-importlib.reload(fcts)
-importlib.reload(f0)
+# importlib.reload(fcts)
+# importlib.reload(f0)
 
 #%%
 ################################################
@@ -22,8 +22,8 @@ importlib.reload(f0)
 ################################################
 
 start_time = time()
-bEnSec = (0*60)
-duree = 3600
+bEnSec = 0*3600 + 0*60 + 0
+duree = 0*3600 + 1*60 + 0
 
 samplerate, dataLR = wavfile.read("input/male2.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
@@ -55,15 +55,20 @@ dataCut = fcts.decouperExtraits(data, 2500, 0.1, samplerate, 20000)
 
 dataCutCris, freqArray = fcts.garderUniquementCris(samplerate,0.38,0.6,250,450,f0.freq_from_HPS, dataCut, data)
 for i in range(len(dataCutCris)):   
-    #fcts.aff("cri"+str(i), t, np.asarray(dataCutCris[i]))
+    fcts.aff("cri"+str(i), t, np.asarray(data[d:f]))
     d=dataCutCris[i][0]
     f=dataCutCris[i][1]
     fcts.listen("output/cri"+str(i)+".wav", samplerate, np.asarray(data[d:f]))
-    print("enregistrement: " + str(i))
-    print("f0 -> " + str(np.round(freqArray[i])))
-    print("duration -> " + str((f-d)/samplerate))
-    print("sum -> " + str(np.sum(abs(data[d:f]))))
-    print("________________")
+    print("*************************")
+    print("* enregistrement: " + str(i))
+    print("*************************")
+    print("*   f0 -> " + str(np.round(freqArray[i])))
+    print("*   duration -> " + str((f-d)/samplerate))  
+    print("*   quantité de son à 5% -> " + str(np.sum(abs(data[d:d+int(np.round(0.05*(f-d)))]))/np.sum(abs(data[d:f]))))    
+    print("*************************")
+    print("")
+    print("_________________________")
+    print("")
 
 print(np.mean(freqArray))
 print(np.std(freqArray))

fcts.py

@@ -66,8 +66,23 @@ def garderUniquementCris(sampleRate,tMin, tMax, fMin, fMax, f0Func, dataCut, dat
         if((f-d>nSamplesMin) & (f-d<nSamplesMax)):
             freq = np.round(f0Func(np.asarray(data[d:f]),sampleRate))
             if((freq>fMin) & (freq<fMax)):
-                res.append(dataCut[i])
-                freqs.append(freq)
+                quantiteDeSonA5Pourcents = np.sum(abs(data[d:d+int(np.round(0.05*(f-d)))]))
+                quantiteDeSon = np.sum(abs(data[d:f]))
+                if (quantiteDeSonA5Pourcents / quantiteDeSon > 0.065):
+                    res.append(dataCut[i])
+                    freqs.append(freq)
+                    
+                    listen("output/cri"+str(i)+".wav", sampleRate, np.asarray(data[d:f]))
+                    print("*************************")
+                    print("* enregistrement: " + str(i))
+                    print("*************************")
+                    print("*   f0 -> " + str(freq))
+                    print("*   duration -> " + str((f-d)/sampleRate))  
+                    print("*   quantité de son à 5% -> " + str(np.sum(abs(data[d:d+int(np.round(0.05*(f-d)))]))/np.sum(abs(data[d:f]))))    
+                    print("*************************")
+                    print("")
+                    print("_________________________")
+                    print("")
     
     return res, freqs
 

frequency_estimator.py

@@ -3,11 +3,7 @@ from numpy.fft import rfft
 from numpy import argmax, mean, diff, log, nonzero
 from scipy.signal import blackmanharris, correlate
 from time import time
-import sys
-try:
-    import soundfile as sf
-except ImportError:
-    from scikits.audiolab import flacread
+
 
 from parabolic import parabolic