Use of baseband phase structure to improve the performance of current speech enhancement algorithms

In this study we propose a noise estimation technique based on spectral sparsity, detected by using the harmonic property of voiced segments of the speech. We estimate the frame to frame phase difference for clean speech, in the baseband Short Time Fourier Transform (STFT) domain, from corrupted speech. This estimated frame-to-frame phase difference is used as a means of detecting the noise and harmonic dominant frequency bins in voiced frames. In the unvoiced frames noise is estimated using a Voice Activity Detector (VAD). Using this approach gives better noise estimation for the highly non-stationary noises like babble, restaurant and subway noise. The proposed noise estimation algorithm along with the phase difference as an additional prior is used to extend the standard spectral subtraction algorithm. Estimated baseband phase difference is used to prevent over-suppression of harmonic dominant speech in the voiced frames by adjusting the over-attenuation factor of the spectral subtraction algorithm. We verify the effectiveness of the proposed noise estimation technique when used with the log-Minimum Mean Squared Error Short Time Spectral Amplitude Estimator (log-MMSE STSA) speech enhancement algorithm. Finally, spectral subtraction and log-MMSE STSA are combined to achieve better noise suppression with minimum musical noise and speech distortion. This combination results in further improvement of speech quality.