Sparsity-based phase spectrum compensation for speech enhancement algorithm

The phase spectrum compensation speech enhancement algorithm compresses the noise by adjusting the phase spectrum to improve the quality of the reconstructed signal. For the traditional phase spectrum compensation (PSC) speech enhancement algorithm, a fixed phase compensation factor is adopted and the performance of the algorithm is easily affected by the accuracy of the noise estimation. A sparsity-based phase spectrum compensation (SPSC) for speech enhancement algorithm is proposed. Firstly, the magnitude spectrum of noise is obtained by using the noise estimation algorithm, the speech enhancement algorithm based on magnitude spectrum is used to obtain the magnitude spectrum of the target speech. Then, the spectro-temporal sparsity is estimated by the local signal-to-noise Ratio (SNR), which is obtained by the magnitude spectrum of the noise and target speech. Then, the sigmoid function is used to improve the phase compensation factor, and the SPSC function is obtained based on the compensation factor combined with the spectro-temporal sparsity. Finally, the SPSC function is used to compensate the spectral components in the phase spectrum, and the speech signal is finally enhanced by the inverse short-time Fourier transform. Simulation experimental results show that under the four different background noise with low SNR, the new phase spectrum compensation algorithm obtains better LSD, PESQ and segSNR indices, it shows that the new algorithm can effectively restore the speech components in the noisy speech under the low SNR, which has a significant effect on noise suppression, and improves the quality of speech and the audibility to some extent.