# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 4 Results and Discussion
### Ablation Study
#### 4.1.1 Performance Gains of Unidirectional Information Interaction Scheme  
As shown in Table 1, we systematically compare the proposed UniInterNet with two baselines in terms of causality, model size, computational complexity, and performance metrics. From the results in Table 1, one can make the following observations.  
First, when only a single speech prediction branch is utilized, i.e., SiNet, its performance is clearly worse than that of UniInterNet. Therefore, adding a noise branch with unidirectional information flow effectively promotes the optimization of the speech branch. This results in improvements in WB-PESQ, CSIG, CBAK, and COVL by 0.15, 0.15, 0.10,  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Causal** & \
\begin{tabular}{c} **Param.** \\ **(M)** \\ \end{tabular} &
\begin{tabular}{c} **MACs** \\ **(G/s)** \\ \end{tabular} & **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 SiNet & \(\blacktriangledown\) & 1.65 & 6.24 & 2.90 \(\pm\) 0.72 & 4.17 \(\pm\) 0.71 & 3.47 \(\pm\) 0.57 & 3.55 \(\pm\) 0.75 \\ \hline BiInterNet & \(\blacktriangledown\) & 7.06 & 24.61 & 3.10 \(\pm\) 0.71 & 4.37 \(\pm\) 0.59 & 3.59 \(\pm\) 0.52 & 3.76 \(\pm\) 0.67 \\ \hline UniInterNet-CausalNoise & \(\blacktriangledown\) & 1.65 & 6.24 & 3.00 \(\pm\) 0.66 & 4.27 \(\pm\) 0.62 & 3.50 \(\pm\) 0.55 & 3.65 \(\pm\) 0.67 \\ UniInterNet & \(\blacktriangledown\) & 1.65 & 6.24 & 3.05 \(\pm\) 0.71 & 4.32 \(\pm\) 0.60 & 3.57 \(\pm\) 0.55 & 3.70 \(\pm\) 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Ablation study with respect to the proposed unidirectional information interaction scheme.  
and 0.15, respectively. Meanwhile, since the noise branch and unidirectional interaction module of UnInterNet are not required during the model inference stage, UnInterNet has the same deployment cost as SiNet.  
Second, the dual-branch network with bidirectional information communication, namely, BiInterNet, indeed achieves higher objective evaluation scores than UniInterNet. But the number of model parameters for BiInterNet is approximately 4.28 times greater than that of UniInterNet, and the computational cost is about 3.94 times higher. This increase is due to the introduction of the noise branch and the bidirectional interaction modules. However, the performance gain of BiInterNet over UniInterNet is relatively limited. In a word, UniInterNet achieves comparable performance to BiInterNet while reducing inference complexity by about 75%.  
Third, if the noise branch of UniInterNet switches to the causal setting, i.e., UniInterNet-CausalNoise in Table 1, it will lead to performance degradation. Since the causality of the noise branch has no influence on algorithm delay and computational complexity during model deployment, it is better to adopt a non-causal setting for the noise branch of UniInterNet.  
Moreover, a comparison of the spectrum of baselines and the proposed UniInterNet is shown in Figure 5. One can see that there is still obvious residual noise in the spectrum processed by SiNet, and the spectrum processed by UniInterNet-CausalNoise also has similar problems. In contrast, the enhanced spectrum by BiInterNet and UniInterNet exhibit more similar TF patterns to the clean speech. Furthermore, the differences between the spectrum processed by BiInterNet and UniInterNet are not significant, although the former yields relatively more ideal results. These observations are generally consistent with the quantitative comparison results mentioned above.