## 4 Experiments and Performance Evaluation
### Results
#### 4.3.2 Ablation Study  
To validate the effectiveness of the proposed modules, comprehensive ablation studies were conducted in a practical deployment setting. Table 3 summarizes the ablation results of the TSDCA-BA model. The experiments individually assessed the contributions of key architectural components, including the TSDCA-BA module, Squeeze-and-Excitation (SE) block, and the single-frame statistical feature extraction mechanism. The baseline configuration, Bi-GRU-Conv\(\times\)1, contains only 24K parameters and achieves a PESQ score of 2.44 and STOI of 0.8904. Increasing the model depth to Bi-GRU-Conv\(\times\)2 significantly improves performance (PESQ 2.60, STOI 0.9026), demonstrating the advantage of deeper recurrent convolutional fusion for temporal context modeling. Introducing the SDCA module into the Bi-GRU-Conv\(\times\)3 architecture further raises PESQ to 2.68 and STOI to 0.9030, confirming the module's efficacy in enhancing temporal dynamics representation. Adding the SE module on top of SDCA (Bi-GRU-Conv\(\times\)3 + SDCA + SE) increases PESQ to 2.79, while STOI slightly decreases to 0.8894, possibly due to overfitting towards perceptual quality metrics. Notably, incorporating single-frame statistical features alongside SDCA and SE  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **PESQ** & **STOI** & **MMACs** \\ \hline noisy & 1.97 & 0.921 & — \\ RNNoise & 2.5 & 0.92 & 40 \\ DCCRN-lite & 2.9 & 0.94 & 150 \\ GTCRN & 2.87 & 0.93 & 33 \\ FPSEN & 2.97 & 0.94 & 89 \\ LisenNET & 2.95 & **0.95** & 56 \\
**TSDCA-BA** & 2.955 & 0.904 & 0.22 \\
**TSDCA-BA-PP\({}^{*}\)** & **3.1** & 0.898 & **0.22** \\ \hline \hline \end{tabular}  
* PP\({}^{*}\) represents the performance of the post-processing stage. The bold text indicates the best performance.  
\end{table}
Table 2: Performance of the model under actual deployment.  
Figure 7: Spectrogram comparison between the proposed TSDCA-BA model and the representative RNNoise model on the test set.  
(Bi-GRU-Conv\(\times\)3 + SDCA + SE + statistical features) further improves PESQ to 2.85 but reduces STOI to 0.8745, indicating a trade-off between speech quality and intelligibility in certain configurations. To ensure fairness, we evaluated the changes in computational load while maintaining the model's lightweight design. The results show that as modules are added to the model, the computational complexity (measured in MMACs) does not increase significantly, remaining approximately at 0.22 MMACs, which demonstrates the model's excellent computational efficiency. In contrast, the combination of only SDCA and SE modules without statistical features achieves a PESQ of 2.62 and a higher STOI of 0.9116, suggesting that the single-frame statistical features may hinder STOI improvement. Ultimately, the full model (Bi-GRU-Conv\(\times\)3 + TSDCA-BA + SE + statistical features) achieves the highest PESQ of 2.92 and STOI of 0.9045, with a modest parameter increase to 112.5 K. This configuration effectively leverages the synergy of global temporal dynamics, channel attention, and frame-level statistical features, resulting in substantial improvements in both perceptual quality and intelligibility while maintaining computational efficiency.