## 4 Experiments and Performance Evaluation
### Results  
#### 4.3.1 Comparison with Baseline Models  
Figure 7 shows a spectrogram comparison on the test set, highlighting the performance differences between the proposed TSDCA-BA model and the representative RNNoise model in terms of noise suppression. Subfigure (a) illustrates the spectrogram of the noisy input, (b) shows the spectrogram after denoising with RNNoise, (c) corresponds to the output of the proposed TSDCA-BA model, and (d) depicts the spectrogram of the clean reference signal. As observed, the TSDCA-BA model achieves more effective noise reduction while better preserving the spectral structure of speech compared to RNNoise. Table 2 shows a comprehensive comparison of the proposed models, TSDCA-BA and TSDCA-BA-PP, with several state-of-the-art speech enhancement models under real deployment scenarios. The evaluation focuses on three key metrics: Perceptual Evaluation of Speech Quality (PESQ), Short-Time Objective Intelligibility (STOI), and computational complexity measured in MMACs. In terms of PESQ, TSDCA-BA-PP achieves the highest score of 3.1, outperforming all baseline models, including FPSEN (2.97), DCCRN-lite (2.9), and LisenNET (2.95). These results indicate that, after the post-processing stage, the proposed model achieves a noticeable improvement in perceived speech quality compared to other baseline methods. In the context of hearing aids, this means the model not only enhances PESQ scores but also retains important environmental information, thereby improving the listening experience for users with hearing impairments. Even without post-processing, TSDCA-BA delivers a strong PESQ score of 2.955, outperforming models such as LisenNET, GTCRN, and RNNoise. Regarding speech intelligibility (STOI), TSDCA-BA achieves a score of 0.904, which is slightly lower than LisenNET (0.95) and DCCRN-lite (0.94). However, given the need to preserve essential background cues, this result remains within a practical and acceptable range. Notably, the post-processing stage in TSDCA-BA-PP leads to a small drop in STOI to 0.898, likely due to the stronger enhancement effects applied to the final output. A major advantage of the proposed models lies in their computational efficiency. Both TSDCA-BA and TSDCA-BA-PP operate at only 0.22 MMACs, which is two orders of magnitude lower than DCCRN-lite (150 MMACs), and significantly below GTCRN (33 MMACs) and LisenNET (56 MMACs). Practical testing on low-power processors reveals that GTCRN cannot maintain real-time performance under such conditions, with the denoising module frequently becoming unresponsive. In contrast, the proposed model maintains a stable latency of around 300 milliseconds, even on low-power devices. These results highlight the suitability of the proposed approach for deployment in resource-constrained or real-time applications, such as hearing aids and edge devices.  
The results demonstrate that TSDCA-BA, particularly when integrated with post-processing, achieves an excellent balance between speech quality and computational cost. Although slight compromises in intelligibility are observed, these trade-offs are justified by the substantial improvements in PESQ and computational efficiency. Furthermore, the proposed approach offers a viable pathway for future research on single-frame inference models.