## 6 Conclusions  
In this paper, we propose an ultra-low computational complexity speech denoising model that integrates single-frame feature concatenation with a lightweight SDCA-BA module. The model employs an efficient single-frame feature extraction module to derive four statistical descriptors--mean, standard deviation, maximum, and minimum--from single-frame spectra, which are then fused with the original features to enhance the representation capability of single-frame spectral features. Subsequently, the lightweight SDCA-BA module utilizes a residual structure, channel attention, and band-wise weighting strategies to effectively partition frequency bands and apply differentiated weighting, further strengthening the representation of key frequency bands. Additionally, the channel SE attention mechanism adaptively enhances critical speech features, effectively suppressing background noise interference. This approach significantly improves the model's feature representation and overall denoising performance without increasing the parameter count. The proposed model substantially reduces computational resource consumption while maintaining high speech enhancement quality, offering a practical solution for achieving low-latency, low-power, and high-performance speech processing on edge devices. Ex  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Platform** & **Latency** & **Memory Usage** & **Power (W)** & **PESQ** \\ \hline python & 3 ms & 200 M & - & 1.2897 (noisy) \\ C++ & 3 ms & 5\(\sim\)10 M & 0.176 & 2.0964 (enhanced) \\ \hline \end{tabular}
\end{table}
Table 4: Performance of the model under actual deployment.  
perimental results demonstrate that on the VCTK-DEMAND dataset, the proposed model achieves a PESQ score improvement of approximately 4.9% over GTCRN and LiSenNET, approaching the performance level of the FSPEN model. Meanwhile, it delivers roughly a 150-fold improvement in computational efficiency (MMACs) compared to existing baseline models. These results highlight the superior balance of performance and efficiency of the proposed approach, validating its potential and advantages for real-world applications in resource-constrained environments. Despite these advantages, some limitations remain. The proposed model employs moderate background noise suppression, which introduces certain drawbacks. The processed audio volume may decrease, and denoising performance may degrade in highly complex noise environments. However, for visually impaired users, fully eliminating environmental noise is not always the optimal solution. Instead, selectively filtering high-frequency noise while preserving ambient sounds helps users better perceive their surroundings. Thus, the model balances noise reduction with the preservation of important acoustic details, making it better suited to the practical needs of this user group. Future work will focus on addressing these limitations to further enhance the model's robustness and adaptability under diverse environmental conditions.  
Conceptualization, Z.F.; methodology, Z.F.; software and optimization, Z.F.; validation, Z.G.; formal analysis, Z.F. and Z.G.; investigation, Z.F. and Z.G.; data curation, Z.F. and Y.L.; writing--original draft preparation, Z.F.; writing--review and editing, Z.F. and Y.L.; model architecture illustration, Z.F. and Y.L.; supervision, J.K.; project administration, J.K.; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no conflicts of interest.