# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 4 Results and Discussion
### Performance Analysis over Different Signal-to-Noise Ratios (SNRs)  
As shown in Table 3, we investigate the performance of the proposed UniInterNet and baseline models across various SNRs. The performance evaluation results presented in Table 3 lead to the following findings: (1) Compared to SiNet, both BiInterNet and the proposed UniInterNet achieve significant performance improvements over different SNRs, with the performance gains in the low SNR range generally being larger than those in the high SNR range. (2) The performance metrics scores of the proposed UniInterNet are generally lower than those of BiInterNet across different SNRs, but as the SNR increases, the performance gap between UniInterNet and BiInterNet narrows overall. This implies that when the noise component is weak, the benefit of the interactive information from the noise prediction branch to the speech prediction branch may decrease. (Note: The standard deviation data for Table 3 are presented in Table A1 in the Appendix A.) To further highlight the advantages of our proposed UniInterNet over previous methods based on bidirectional interactive speech and noise prediction, we perform a comprehensive performance comparison with SN-Net [29]. SN-Net consists of parallel speech and noise prediction branches, with bidirectional information interaction modules placed between them. These modules extract speech or noise-related features, facilitating more accurate predictions of speech and noise signals. The performance comparison results of our proposed scheme and SN-Net are presented in Table 4. For SN-Net, we not only cite the evaluation metric scores from the original paper but also reproduce this method and calculate the performance evaluation results ourselves to ensure a fair comparison. Our reproduced results closely match those reported in the original paper across all evaluation metrics, confirming the correctness of our reproduction of SN-Net. Due to the utilization of self-attention for global dependency modeling along the temporal dimension, as well as the non-causal convolutional and deconvolutional layers, SN-Net is a non-causal model. As a result, SN-Net requires \begin{table}
\begin{tabular}{l c c c} \hline  & **WB-PESQ** & **CSIG** & **CBAK** & **COVL** \\ \hline noisy & 1.97 \(\pm\) 0.75 & 3.35 \(\pm\) 0.87 & 2.44 \(\pm\) 0.67 & 2.63 \(\pm\) 0.83 \\ \hline UniInterNet & 3.05 \(\pm\) 0.71 & 4.32 \(\pm\) 0.60 & 3.57 \(\pm\) 0.55 & 3.70 \(\pm\) 0.67 \\ w/o EncInter & 2.97 \(\pm\) 0.72 & 4.22 \(\pm\) 0.60 & 3.49 \(\pm\) 0.58 & 3.60 \(\pm\) 0.68 \\ w/o RecInter & 2.99 \(\pm\) 0.72 & 4.28 \(\pm\) 0.64 & 3.51 \(\pm\) 0.56 & 3.65 \(\pm\) 0.71 \\ w/o DecInter & 2.94 \(\pm\) 0.67 & 4.21 \(\pm\) 0.60 & 3.47 \(\pm\) 0.57 & 3.59 \(\pm\) 0.66 \\ \hline \end{tabular}
\end{table} 
Table 2: Ablation study on the effects of information interaction at different network parts. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c} \hline  & \multicolumn{4}{c}{**WB-PESQ**} & \multicolumn{4}{c}{**CSIG**} & \multicolumn{4}{c}{**CBAK**} & \multicolumn{4}{c}{**COVL**} \\ \cline{2-13} SNRs (dB) & 2.5 & 7.5 & 12.5 & 17.5 & 2.5 & 7.5 & 12.5 & 17.5 & 2.5 & 7.5 & 12.5 & 17.5 & 2.5 & 7.5 & 12.5 & 17.5 \\ \hline noisy & 1.42 & 1.76 & 2.10 & 2.60 & 2.62 & 3.14 & 3.59 & 4.05 & 1.77 & 2.21 & 2.63 & 3.17 & 1.96 & 2.42 & 2.83 & 3.33 \\ \hline SiNet & 2.31 & 2.80 & 3.08 & 3.43 & 3.50 & 4.09 & 4.40 & 4.67 & 2.98 & 3.37 & 3.61 & 3.92 & 2.88 & 3.45 & 3.76 & 4.10 \\ BiInterNet & 2.54 & 3.05 & 3.27 & 3.58 & 3.87 & 4.33 & 4.53 & 4.75 & 3.15 & 3.52 & 3.72 & 3.98 & 3.20 & 3.70 & 3.92 & 4.21 \\ UniInterNet & 2.46 & 2.92 & 3.21 & 3.57 & 3.80 & 4.25 & 4.49 & 4.73 & 3.09 & 3.46 & 3.71 & 4.01 & 3.13 & 3.60 & 3.87 & 4.19 \\ \hline \end{tabular}
\end{table} 
Table 3: Performance evaluation results over different signal-to-noise ratios (SNRs). the future speech information to predict the current enhanced speech frame. Moreover, the dual-branch structure with self-attention contributes to a large model size and high computational complexity in SN-Net.