# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 5 Conclusions  
In this work, we design a novel SE model architecture based on a dual-branch network and auxiliary noise prediction. A unidirectional interaction module is proposed and inserted between the two branches to transfer useful information from the speech branch to the noise branch. As a result, the noise branch is not needed during the model deployment phase, which reduces the deployment cost compared to previous bidirectional interactive speech and noise modeling-based methods. Additionally, the speech branch is designed to be causal for small algorithm delay, while the noise branch adopts a non-causal design to improve the noise modeling accuracy and better assist the optimization of the speech branch. Therefore, UniInterNet offers great advantages over existing bidirectional interactive speech and noise modeling-based benchmark in terms of inference delay. Experimental results demonstrate the effectiveness of our design, where the proposed UniInterNet maintains comparable performance to the traditional dual-branch bidirectional interactive methods while significantly reducing the model inference complexity. Moreover, we find that \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Causal** & **Input** & **WB-PESQ** & **NB-PESQ** & **STOI (\%)** & **SI-SNR (dB)** \\ \hline noisy & - & - & 1.58 & 2.45 & 91.52 & 9.07 \\ NSNet [34] & \(\blacktriangledown\) & Log-power & 2.15 & 2.87 & 94.47 & 15.61 \\ SICRN [19] & \(\blacktriangledown\) & Complex & 2.62 & 3.23 & 95.83 & 16.00 \\ CTS-Net [8] & \(\blacktriangledown\) & Complex & 2.94 & 3.42 & 96.66 & 17.99 \\ FullSubNet+ [24] & \(\blacktriangledown\) & Complex+Magnitude & 2.98 & 3.50 & 96.69 & 18.34 \\ Inter-SubNet [26] & \(\blacktimes\) & Magnitude & 3.00 & 3.50 & 96.61 & 18.05 \\ FS-CANet [48] & \(\blacktriangledown\) & Magnitude & 3.02 & 3.51 & 96.74 & 18.08 \\ SEnet+Ran-net [49] & \(\blacktimes\) & Magnitude & 3.16 & 3.57 & - & - \\ GaGNet [20] & \(\blacktriangledown\) & Complex & 3.17 & 3.56 & 97.13 & **18.91** \\ UniInterNet (ours) & \(\blacktriangledown\) & STDCT & **3.26** & **3.59** & **97.16** & 18.28 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance comparison results with existing advanced systems on the DNS-Challenge dataset. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Causal** & **Input** & **WB-PESQ** & **CSIG** & **CBAK** & **COVL** \\ \hline noisy & - & - & 1.97 & 3.35 & 2.44 & 2.63 \\ DCCRN [7] & \(\blacktriangledown\) & Complex & 2.68 & 3.88 & 3.18 & 3.27 \\ CompNet [21] & \(\blacktriangledown\) & Time+Complex & 2.90 & 4.16 & 3.37 & 3.53 \\ LFSFNet [25] & \(\blacktriangledown\) & Magnitude & 2.91 & - & - & - \\ CTS-Net [8] & \(\blacktriangledown\) & Complex & 2.92 & 4.25 & 3.46 & 3.59 \\ DEMUCS [1] & \(\blacktriangledown\) & Time & 2.93 & 4.22 & 3.25 & 3.52 \\ GaGNet [20] & \(\blacktriangledown\) & Complex & 2.94 & 4.26 & 3.45 & 3.59 \\ NASE [27] & \(\blacktimes\) & Complex & 3.01 & - & - & - \\ FDFNet [22] & \(\blacktriangledown\) & STDCT & **3.05** & 4.23 & 3.55 & 3.65 \\ UniInterNet (ours) & \(\blacktriangledown\) & STDCT & **3.05** & **4.32** & **3.57** & **3.70** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison results with existing advanced systems on the VoiceBank+ DEMAND dataset. the unidirectional information interaction between the two branches is necessary in all processing stages, including the high-level feature extraction at the encoder, the sequence modeling analysis at the recurrent module, and the target mask estimation at the decoder. Removing the unidirectional interaction module at any one of the three parts results in performance degradation. Among the three parts, the information interaction at the decoders is the most important. Eventually, the proposed UniInterNet outperforms the previous advanced methods on both VoiceBank+DEMAND and DNS-Challenge datasets. In the future, we plan to integrate other related tasks into this proposed framework, such as speech dereverberation, declipping, and super-resolution.