Single-channel Speech Dereverberation via Generative Adversarial Training
Table 3: Comparison of dereverberation systems with different methods.
['3 System', 'PESQ Room D', 'PESQ Room E', 'SRMR Room D', 'SRMR Room E']
We conduct several baseline systems. A standard WPE-based system and a deep neural network-based system, CBLDNN, are conducted. CBLDNN outputs PSM-based mask, and MSE is selected as loss function. CBLDNN has the same model structure as the generator of DeReGAT. In this section, we explore the impact of GAT. In GAT, PSM is used to measure the loss of L1, which is utilized to minimize the distance between generations and clean examples. Compared with existing methods, PESQ and SRMR of DeReGAT have been significantly improved. The SRMR of speech processed by DeReGAT is markedly approximate to the clean speech. The results mean GAT plays an important role in improving the performance. GAT makes the speech produced by generator approaches to clean one. The results also state that our DeReGAT achieves better dereverberation performance in variant environments.