Correction of the bug on the segmentation from value

Comments on the 4Hz Irit Speech segmentor

diff --git a/timeside/analyzer/irit_speech_4hz.py b/timeside/analyzer/irit_speech_4hz.py
index 9605e33825485950cc25b79c9b84cc5e50fe10d0..5658916e5ca8958c00d060bdb2b7a937eefc3719 100644 (file)
--- a/timeside/analyzer/irit_speech_4hz.py
+++ b/timeside/analyzer/irit_speech_4hz.py
@@ -31,21 +31,42 @@ from matplotlib import pylab
 
 class IRITSpeech4Hz(Processor):
     implements(IValueAnalyzer)
+    '''
+    Segmentor based on the analysis of the 4Hz energy modulation.
+
+    Properties:
+               - energy4hz             (list)          : List of the 4Hz energy by frame for the modulation computation
+               - threshold             (float)         : Threshold for the classification Speech/NonSpeech
+               - frequency_center      (float)         : Center of the frequency range where the energy is extracted
+               - frequency_width       (float)         : Width of the frequency range where the energy is extracted
+               - orderFilter           (int)           : Order of the pass-band filter extracting the frequency range
+               - normalizeEnergy       (boolean)       : Whether the energy must be normalized or not 
+               - nFFT                          (int)           : Number of points for the FFT. Better if 512 <= nFFT <= 2048
+               - nbFilters                     (int)           : Length of the Mel Filter bank 
+               - melFilter             (numpy array)   : Mel Filter bank 
+               - modulLen                      (float)         : Length (in second) of the modulation computation window 
+    '''
 
     @interfacedoc
     def setup(self, channels=None, samplerate=None, blocksize=None, totalframes=None):
         super(IRITSpeech4Hz, self).setup(channels, samplerate, blocksize, totalframes)
         self.energy4hz = []
+        print "top"
+        # Classification
         self.threshold = 2.0
-        self.smoothLen = 5
-        self.fCenter = 4.0
+        
+        # Pass-band Filter 
+        self.frequency_center = 4.0
+        self.frequency_width = 0.5
+        self.orderFilter=100
+
+
         self.normalizeEnergy = True
         self.nFFT=2048
-        self.orderFilter=100
         self.nbFilters =30
-        self.modulLen = 2
-        self.fwidth = 0.5
+        self.modulLen = 2.0
         self.melFilter = melFilterBank(self.nbFilters,self.nFFT,samplerate);
+
     @staticmethod
     @interfacedoc
     def id():
@@ -65,45 +86,56 @@ class IRITSpeech4Hz(Processor):
         return "Speech confidences indexes"
 
     def process(self, frames, eod=False):
-        '''
-
-        '''
+               '''
+                               
+               '''
+               
+               frames = frames.T[0]
+               # windowing of the frame (could be a changeable property)
+               w = frames * hamming(len(frames));
+               
+               # Mel scale spectrum extraction
+               f = abs(rfft(w,n=2*self.nFFT)[0:self.nFFT])
+               e = dot(f**2,self.melFilter)
+               
+               self.energy4hz.append(e)
+               
+               return frames, eod
         
-        frames = frames.T[0]
-        w = frames * hamming(len(frames))
-        f = abs(rfft(w, n=2*self.nFFT)[0:self.nFFT])
-        e = dot(f**2, self.melFilter)
-        self.energy4hz.append(e)
-        return frames, eod
-
     def results(self):
-        '''
-
-        '''
-        #wavwrite('out.wav',self.fe,(numpy.array(self.data)*2**15).astype(numpy.int16))
-
-        Wo = self.fCenter/self.samplerate()
-        Wn = [ Wo-(self.fwidth/2)/self.samplerate() , Wo+(self.fwidth/2)/self.samplerate()]
-        num = firwin(self.orderFilter, Wn, pass_zero=False)
-        self.energy4hz=numpy.array(self.energy4hz)
-        energy = lfilter(num, 1, self.energy4hz.T, 0)
-        energy = sum(energy)
-
-        if self.normalizeEnergy:
-            energy = energy / mean(energy)
-
-        w = int(float(self.modulLen) * self.samplerate() / self.blocksize())
-        modEnergyValue = computeModulation(energy, w, True)
-
-        conf = array(modEnergyValue-self.threshold)/self.threshold
-        conf[conf>1] = 1
-
-        modEnergy = AnalyzerResult(id = "irit_4hzenergy_confidence", name = "modulation energie (IRIT)", unit = "?")
-        modEnergy.value = conf
-        convert = {False:'NonSpeech',True:'Speech'}
-
-        segList = segmentFromValues(modEnergyValue>self.threshold)
-        segmentsEntropy =[]
+       '''
+               
+       '''     
+       print "Results"
+       # Creation of the pass-band filter      
+       Wo = self.frequency_center/self.samplerate()  ;
+       Wn = [ Wo-(self.frequency_width/2)/self.samplerate() , Wo+(self.frequency_width/2)/self.samplerate()];
+       num = firwin(self.orderFilter, Wn,pass_zero=False);
+               
+               
+       # Energy on the frequency range 
+       self.energy4hz=numpy.array(self.energy4hz)              
+       energy = lfilter(num,1,self.energy4hz.T,0)
+       energy = sum(energy)
+               
+       # Normalization
+       if self.normalizeEnergy :
+               energy =energy/mean(energy)
+                       
+       # Energy Modulation
+       frameLenModulation = int(self.modulLen*self.samplerate()/self.blocksize())
+       modEnergyValue =computeModulation(energy,frameLenModulation,True)
+               
+       # Confidence Index      
+       conf = array(modEnergyValue-self.threshold)/self.threshold
+       conf[conf>1] = 1
+
+       modEnergy = AnalyzerResult(id = "irit_4hzenergy_confidence", name = "modulation energie (IRIT)", unit = "?")
+       modEnergy.value = conf
+       convert = {False:'NonSpeech',True:'Speech'}
+               
+       segList = segmentFromValues(modEnergyValue>self.threshold)
+       segmentsEntropy =[]
         for s in segList : 
             segmentsEntropy.append((numpy.float(s[0])*self.blocksize()/self.samplerate(),
                                     numpy.float(s[1])*self.blocksize()/self.samplerate(),

diff --git a/timeside/analyzer/utils.py b/timeside/analyzer/utils.py
index 0192ca7c10d4e0d0e8da5c9bc82342bbcf7c9041..27a1c3531b234302ad88933a3492c1f93f03aeb9 100644 (file)
--- a/timeside/analyzer/utils.py
+++ b/timeside/analyzer/utils.py
@@ -74,11 +74,11 @@ def segmentFromValues(values,offset=0):
     for i,v in enumerate(values) :
 
         if not (v == seg[2]) :
-            seg[1] = i+offset
+            seg[1] = i+offset-1
             segList.append(tuple(seg))
             seg = [i+offset,-1,v]
 
-    seg[1] = i+offset+1
+    seg[1] = i+offset
     segList.append(tuple(seg))
 
     return segList