# Speech Enhancement Based on Unidirectional Interactive Noise Modeling Assistance
## 4 Results and Discussion
### Performance Comparison with Existing Advanced Systems  
We have also conducted the performance comparison with existing advanced methods on both VoiceBank+DEMAND and DNS-Challenge datasets. These benchmarks take various types of input, including time-domain waveform, STFT magnitude spectrum, STFT complex spectrum, etc.  
The comparison results on the VoiceBank+DEMAND dataset are illustrated in Table 5. Eight different SE systems are selected for comparative analysis. The first category of systems directly processes the noisy waveform using DNN to predict the enhanced signal in the time domain, such as DEMUCS [1]. The second category includes methods that operate on the STFT complex-valued spectrum with a CRN (i.e., DCCRN [7]) or a diffusion model (i.e., NASE [27]). The third category consists of methods employing the two-stage technique for both coarse magnitude spectrum estimation and fine-grained complex-valued STFT spectrum refinement, including CTS-Net [8] and GaGNet [20]. In addition, multi-domain processing methods, such as CompNet [21] and FDFNet [22], as well as full-band and sub-band fusion system like LFSFNet [25], are also included. The proposed UniInterNet outperforms the benchmarks across all the evaluation metrics. It should be noted that although the performance gain of UniInterNet is relatively modest compared to FDFNet [22], the model parameters and computational cost of FDFNet are 4.43 M and 10.66 GMACs/s, respectively, which are significantly higher than those of UniInterNet.  
For the DNS-Challenge dataset, we select eight advanced systems as benchmarks for comparison: NSNet [34] takes the log-power spectrum as input and employs a neural network based on RNN; SICRN [19] follows the CRN architecture while preserving the inherent frequency structure of the signal using a state space model and inplace convolution; CTS-Net [8] and GaGNet [20] adopt the two-stage framework for enhanced performance; FullSubNet+ [24], Inter-SubNet [26], and FS-CANet [48] incorporate both local sub-band spectrum features and global cross-band correlations; and SENet+Ran-net [49] imitates human cognitive behavior by separating the regular components of noise from its random components. The results in Table 6 demonstrate that UniInterNet achieves the best scores  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Causal** & **Param. (M)** & **MACs (G/s)** & **WB-PESQ** & **CSIG** & **CBAK** & **COVL** \\ \hline noisy & - & - & - & 1.97 & 3.35 & 2.44 & 2.63 \\ \hline SN-Net (original paper) & \(\bigstar\) & 7.22 & 30.51 & 3.12 & 4.39 & 3.60 & 3.77 \\ SN-Net (our reproduction) & \(\bigstar\) & 7.22 & 30.51 & 3.13 & 4.33 & 3.63 & 3.76 \\ \hline BiInterNet & \(\bigstar\) & 7.06 & 24.61 & 3.10 & 4.37 & 3.59 & 3.76 \\ UniInterNet & \(\bigstar\) & 1.65 & 6.24 & 3.05 & 4.32 & 3.57 & 3.70 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison results with previous methods based on bidirectional interactive speech and noise prediction, specifically SN-Net [29].  
on most of the evaluation metrics, except that GaGNet [20] performs better on SI-SNR. These findings underscore the effectiveness of the proposed UniInterNet.  
The processed audio samples of the VoiceBank+DEMAND and DNS-Challenge datasets can be found at [[https://github.com/Zhangyuewei98/UniInterNet](https://github.com/Zhangyuewei98/UniInterNet)]([https://github.com/Zhangyuewei98/UniInterNet](https://github.com/Zhangyuewei98/UniInterNet)) accessed on 18 December 2024.