音声形式を除外形式に追加

音声強調クラス群のリファクタリング

.hgignore

@@ -5,4 +5,7 @@ syntax: glob
 *~
 *.wave
 *.wav
+*.mp3
+*.ogg
+*.raw
 #*

voice_enhancement.py

@@ -9,18 +9,18 @@ def __init__(self,winsize,window,ratio=2.0):
         self._ratio=ratio
 
     def compute(self,signal,noise):
-        SY = sp.fft(signal*self._window)
-        SYr = sp.absolute(SY)**2.0
-        SYp = sp.angle(SY)
-        NY = sp.fft(noise*self._window)
-        NYr = sp.absolute(NY)**2.0
-        SYr = SYr - NYr*self._ratio
-        SYr = sp.maximum(SYr,0)
-        SYr = sp.sqrt(SYr)
-        SY = SYr * sp.exp(SYp*1j)
-        return sp.real(sp.ifft(SY))
+        s_spec = sp.fft(signal*self._window)
+        s_amp = sp.absolute(s_spec)**2.0
+        s_phase = sp.angle(s_spec)
+        n_spec = sp.fft(noise*self._window)
+        n_amp = sp.absolute(n_spec)**2.0
+        s_amp = s_amp - n_amp*self._ratio
+        s_amp = sp.maximum(s_amp,0)
+        s_amp = sp.sqrt(s_amp)
+        s_spec = s_amp * sp.exp(s_phase*1j)
+        return sp.real(sp.ifft(s_spec))
 
-class SpectrumReconstruction():
+class SpectrumReconstruction(object):
     def __init__(self,winsize,window,alpha=0.98):
         self._window=window
         self._G = sp.zeros(winsize,sp.float32)
@@ -30,62 +30,56 @@ def __init__(self,winsize,window,alpha=0.98):
     def compute(self,signal,noise):
         return signal
 
-    def _calc_aposteriori_snr(self,syr,nyr):
-        return syr**2.0/nyr**2.0
+    def _calc_aposteriori_snr(self,s_amp,n_amp):
+        return s_amp**2.0/n_amp**2.0
 
     def _calc_apriori_snr(self,gamma):
         return self._alpha*self._G**2 * self._prevGamma + (1.0-self._alpha)*sp.maximum(gamma-1.0, 0)#a priori s/n ratio
 
 class MMSE_STSA(SpectrumReconstruction):
-    def __init__(self,winsize,window,alpha=0.98):
-        self._window=window
-        self._G = sp.zeros(winsize,sp.float32)
-        self._prevGamma = sp.zeros(winsize,sp.float32)
-        self._alpha = alpha
+    def __init__(self,winsize,window,alpha=0.99):
         self._gamma15=spc.gamma(1.5)
+        super(self.__class__,self).__init__(winsize,window,alpha)
 
     def compute(self,signal,noise):
-        SY = sp.fft(signal*self._window)
-        SYr = sp.absolute(SY)
-        SYp = sp.angle(SY)
-        NY = sp.fft(noise*self._window)
-        NYr = sp.absolute(NY)
-        gamma = self._calc_aposteriori_snr(SYr,NYr)
+        s_spec = sp.fft(signal*self._window)
+        s_amp = sp.absolute(s_spec)
+        s_phase = sp.angle(s_spec)
+        n_spec = sp.fft(noise*self._window)
+        n_amp = sp.absolute(n_spec)
+        gamma = self._calc_aposteriori_snr(s_amp,n_amp)
         xi = self._calc_apriori_snr(gamma)
         self._prevGamma = gamma
         nu = gamma * xi / (1+xi)
         self._G = (self._gamma15*sp.sqrt(nu)/gamma)*sp.exp(-nu/2)*((1+nu)*spc.i0(nu/2)+nu*spc.i1(nu/2))
         idx = sp.isnan(self._G) + sp.isinf(self._G)
         #self._G[idx] = xi[idx] / ( xi[idx] + 1)
         self._G[idx] = 0.0
-        Yr = self._G * SYr
-        Yr = sp.maximum(Yr,0)
-        Y = Yr * sp.exp(SYp*1j)
-        return sp.real(sp.ifft(Y))
+        amp = self._G * s_amp
+        amp = sp.maximum(amp,0)
+        spec = amp * sp.exp(s_phase*1j)
+        return sp.real(sp.ifft(spec))
 
 class JointMap(SpectrumReconstruction):
     def __init__(self,winsize,window,alpha=0.99,mu=1.74,tau=0.126):
-        self._window=window
-        self._G = sp.zeros(winsize,sp.float32)
-        self._prevGamma = sp.zeros(winsize,sp.float32)
-        self._alpha = alpha
         self._mu = mu
         self._tau = tau
+        super(self.__class__,self).__init__(winsize,window,alpha)
 
     def compute(self,signal,noise):
-        SY = sp.fft(signal*self._window)
-        SYr = sp.absolute(SY)
-        SYp = sp.angle(SY)
-        NY = sp.fft(noise*self._window)
-        NYr = sp.absolute(NY)
-        gamma = self._calc_aposteriori_snr(SYr,NYr)
+        s_spec = sp.fft(signal*self._window)
+        s_amp = sp.absolute(s_spec)
+        s_phase = sp.angle(s_spec)
+        n_spec = sp.fft(noise*self._window)
+        n_amp = sp.absolute(n_spec)
+        gamma = self._calc_aposteriori_snr(s_amp,n_amp)
         xi = self._calc_apriori_snr(gamma)
         self._prevGamma = gamma
         u = 0.5 - self._mu/(4.0*sp.sqrt(gamma*xi))
         self._G = u + sp.sqrt(u**2.0 + self._tau/(gamma*2.0))
         idx = sp.isnan(self._G) + sp.isinf(self._G)
         #self._G[idx] = xi[idx] / ( xi[idx] + 1)
         self._G[idx] = 0.0
-        Yr = self._G * SYr
-        Y = Yr * sp.exp(SYp*1j)
-        return sp.real(sp.ifft(Y))
+        amp = self._G * s_amp
+        spec = amp * sp.exp(s_phase*1j)
+        return sp.real(sp.ifft(spec))