TFMethods.py 文件源码

def NMREval(self, xn, xnhat):
        """ Method to perform NMR perceptual evaluation of audio quality between two signals.
        Args        :
            xn      :   (ndarray) 1D Array containing the true time domain signal.
            xnhat   :   (ndarray) 1D Array containing the estimated time domain signal.
        Returns     :
            NMR     :   (float)   A float measurement in dB providing a perceptually weighted
                        evaluation. Below -9 dB can be considered as in-audible difference/error.
        As appears in :
        - K. Brandenburg and T. Sporer,  “NMR and Masking Flag: Evaluation of Quality Using Perceptual Criteria,” in
        Proceedings of the AES 11th International Conference on Test and Measurement, Portland, USA, May 1992, pp. 169–179
        - J. Nikunen and T. Virtanen, "Noise-to-mask ratio minimization by weighted non-negative matrix factorization," in
         Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on, Dallas, TX, 2010, pp. 25-28.
        """
        mX, _ = TimeFrequencyDecomposition.STFT(xn, hanning(self.nfft/2 + 1), self.nfft, self.nfft/4)
        mXhat, _ = TimeFrequencyDecomposition.STFT(xnhat, hanning(self.nfft/2 + 1), self.nfft, self.nfft/4)

        # Compute Error
        Err = np.abs(mX - mXhat) ** 2.

        # Acquire Masking Threshold
        mT = self.maskingThreshold(mX)

        # Inverse the filter of masking threshold
        imT = 1./(mT + eps)

        # Outer/Middle Ear transfer function on the diagonal
        LTq = 10 ** (self.MOEar()/20.)

        # NMR computation
        NMR = 10. * np.log10((1./mX.shape[0]) * self._maxb * np.sum((imT * (Err*LTq))))
        print(NMR)
        return NMR