DNN Speech Separation Algorithm Based on Improved Segmented Masking Target
Meng Gao, Ying Gao, Feng Pei · 2021
To further improve the speech separation effect of deep neural networks (DNN), a DNN speech separation algorithm is proposed in this paper based on segmented masking target. The algorithm combines the advantages of IBM and IRM in different signal-to-noise ratio (SNR) regions to construct a segmented masking target that can adapt to changes in SNR as the training target of DNN. In addition, to improve the accuracy of IRM estimation, a two-step prior SNR is used for the effective calculation to further improve the speech separation performance of the DNN model. Finally, the simulation experiments show that the improved target in this paper has a better speech separation effect than IBM and IRM.