MaskingMethods.py 文件源码

def Wiener(self):
        """
            Computation of Wiener-like Mask. As appears in :
            H Erdogan, John R. Hershey, Shinji Watanabe, and Jonathan Le Roux,
            "Phase-sensitive and recognition-boosted speech separation using deep recurrent neural networks,"
            in ICASSP 2015, Brisbane, April, 2015.
        Args:
                sTarget:   (2D ndarray) Magnitude Spectrogram of the target component
                nResidual: (2D ndarray) Magnitude Spectrogram of the residual component
        Returns:
                mask:      (2D ndarray) Array that contains time frequency gain values
        """
        print('Wiener-like Mask')
        localsTarget = self._sTarget ** 2.
        numElements = len(self._nResidual)
        if numElements > 1:
            localnResidual = self._nResidual[0] ** 2. + localsTarget
            for indx in range(1, numElements):
                localnResidual += self._nResidual[indx] ** 2.
        else :
            localnResidual = self._nResidual[0] ** 2. + localsTarget

        self._mask = np.divide((localsTarget + self._eps), (self._eps + localnResidual))