## 1 Introduction  
In recent years, research in artificial intelligence has gradually shifted its focus from large-scale models to small lightweight models (SLMs), aiming to reduce the dependency on GPU computing resources. Due to their reduced parameter counts, compact model sizes, and low computational overhead, SLMs offer viable solutions for deployment in resource-constrained environments while maintaining competitive performance. In the field of audio denoising, various lightweight model architectures have been proposed [1, 2, 3], aiming to lower the number of parameters and computational complexity. However, these methods exhibit certain limitations in denoising performance; they nevertheless provide valuable reference for the design of efficient speech enhancement systems. With continuous advancements in model design, several high-performance lightweight models have emerged. For instance, GTCRN [4] simplifies the DPCRN module and integrates complex mask estimation, significantly reducing parameter count and complexity while optimizing the Equivalent Rectangular Bandwidth (ERB) module to achieve a computational cost of only 33 MMACs. FSPEN [5] employs a combination of full-band and sub-band joint encoding, a dual-path enhancement module (DPE) with path extension, and a multi-scale decoder to achieve ultra-lightweight and efficient real-time speech enhancement. Similarly, Lightweight SE Network (LiSenNet) [6] utilizes lightweight sub-band processing and a dual-path structure, leveraging band decomposition and time-frequency modeling to realize efficient and real-time speech enhancement, achieving both significant parameter reduction and competitive performance. However, most existing lightweight models rely on multi-frame inputs, resulting in higher inference latency, which limits their applicability in real-time hearing aid scenarios where power consumption is constrained and latency must be minimized. In contrast, single-frame inference provides extremely low computational burden but suffers from performance degradation due to the lack of contextual information. To address this limitation, we propose a novel low-complexity speech enhancement model based on single-frame feature concatenation and a lightweight convolution-attention mechanism (SDCA-BA). The proposed model incorporates an efficient single-frame feature extraction module and the SDCA-BA module to effectively fuse multi-scale information, achieving high-quality speech enhancement while significantly reducing computational resource consumption. The result is a low-latency, low-power, and high-performance audio denoising solution. To validate the practicality of the proposed model, we integrated it into a prototype hearing aid device and conducted real-world evaluations. Experimental results show that our model has only 112.3 K parameters and achieves excellent real-time enhancement capabilities with a complexity of 0.22 MMACs. Compared to GTCRN and LiSenNet (33 and 56 MMACs with PESQ scores of 2.87 and 2.95, respectively), and the FSPEN model (approx. 89 MMACs with a PESQ of 2.97), the proposed model achieves superior performance with lower complexity when evaluated on the VoiceBank-DEMAND test set. Our model achieves higher speech quality under a lower computational budget. The proposed system reaches a Perceptual Evaluation of Speech Quality (PESQ) of 2.955 before post-processing, outperforming GTCRN and LiSenNet and approaching FSPEN. After post-processing, the PESQ is further improved to 3.1, outperforming all baseline models with an approximate performance gain of 4.9%. The remainder of this paper is organized as follows: Section 2 reviews related work; Section 3 details the proposed single-frame feature extraction method and its integration with the core modules; Section 4 describes the experimental setup and presents a comparative analysis against baseline models; Section 5 discusses the current state and challenges of deploying lightweight models in hearing aid applications; and Section 6 concludes the paper with future research directions.