# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 4 Results and Discussion
### Ablation Study
#### 4.1.2 Effects of Information Interaction at Different Parts of the Network  
We subsequently investigate the effects of the unidirectional interaction modules between the speech and noise branches at different parts of the network, including the encoder, recurrent module, and decoder. By analyzing the performance variations, we aim  
Figure 5: Visualization of the spectrum of the following: (**a**) noisy speech; (**b**) clean speech; (**c**) enhanced speech by SiNet; (**d**) enhanced speech by BiInterNet; (**e**) enhanced speech by UniInterNet-CausalNoise; (**f**) enhanced speech by UniInterNet. The noise type is open area cafeteria noise.  
to identify the optimal placement for the interaction module to maximize the effectiveness of the unidirectional information flow. Specifically, we have experimented with removing the unidirectional interaction modules from one of the three parts, namely, the encoder, recurrent module, and decoder, while retaining the unidirectional interaction modules in the other two parts. The experimental results are illustrated in Table 2. It can be observed that removing the unidirectional interaction module from any position in the UniInterNet leads to performance degradation. Therefore, the information interactions between the encoders, recurrent modules, and decoders are all necessary for better performance. In addition, the information interaction between decoders is the most critical, as removing the interaction module between the decoders of the speech and noise branches (i.e., UniInterNet w/o DecInter) leads to the most noticeable performance loss. The second most significant impact comes from removing the interaction module between the encoders (i.e., UniInterNet w/o EncInter). In contrast, removing the interaction module between the recurrent modules of the two branches (i.e., UniInterNet w/o RecInter) results in the least performance degradation.  
To better emphasize the advantage of the unidirectional information interaction at different parts, an example of the spectrum of noisy speech, clean speech, and enhanced speech processed by UniInterNet w/o EncInter, UniInterNet w/o RecInter, UniInterNet w/o DecInter, and UniInterNet is presented in Figure 6. We can observe that UniInterNet performs best in terms of background noise suppression and target spectrum recovery. Meanwhile, the residual noise in the spectrum processed by UniInterNet w/o DecInter is more obvious than that in UniInterNet w/o EncInter and UniInterNet w/o RecInter, while the residual noise in the spectrum processed by UniInterNet w/o RecInter is relatively minimal. This trend is generally consistent with the analysis of the quantitative evaluation metrics above.  
Figure 6: Visualization of the spectrum of the following: (**a**) noisy speech; (**b**) clean speech; (**c**) enhanced speech by UniInterNet w/o EncInter; (**d**) enhanced speech by UniInterNet w/o RecInter; (**e**) enhanced speech by UniInterNet w/o DecInter; (**f**) enhanced speech by UniInterNet. The noise type is public square noise.  
In summary, the information interaction between the two branches is beneficial for high-level feature extraction, sequence modeling analysis, and target mask reconstruction, with the greatest impact observed in target mask reconstruction.