## 2 Related Work
### Ultra-Lightweight Speech Enhancement Models  
Conventional deep neural network (DNN)-based speech enhancement models, such as DPATD [15], MetricGAN [16], and CMGAN [17], generally exhibit high computational complexity and large parameter sizes, which hinder their deployment on low-power embedded chips. This limitation is especially critical in real-time applications where power consumption and latency are key constraints. To address these challenges, numerous lightweight speech enhancement models have been proposed, employing techniques including network pruning, parameter quantization, model compression, and multi-scale feature extraction. These approaches aim to significantly reduce computational demands while maintaining satisfactory speech quality. Consequently, lightweight models have become a major focus in the field of low-power speech enhancement research.  
Early representative lightweight models such as RNNoise [18] feature low parameter counts and computational loads but achieve a modest PESQ score of only 2.5 on the VoiceBank-DEMAND dataset, indicating limited noise reduction performance. The DCCRN model [19], an improvement based on CRN, leverages complex-domain processing to enhance recurrent neural network (RNN) and phase modeling capabilities, improving the PESQ to 2.93. However, this improvement comes at the cost of increased parameter size (3.7 M) and computational complexity (150 MMACs). The DeepFilterGAN model [20] introduces a full-band real-time speech enhancement system with a GAN-based stochastic regeneration framework, employing Equivalent Rectangular Bandwidth (ERB) domain features to enhance speech envelopes and reduce parameter count. Although the total parameters remain relatively large at 3.58 M, the ERB-based [21] approach provides a valuable strategy for parameter reduction. With ongoing advancements in model  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DSP Model** & **MMACs** & **Energy** & **Estimated Cost** \\ \hline Cadence HiFi 3z & 540 & 10.8 mW & — \\ Cadence HiFi 4 (Cadence Design Systems, San Jose, CA, USA) & 1080 & 20–25 mW & — \\ XMOS xC-ORE-200 (XMOS Ltd, Bristol, UK) & 1000 & 20 mW & €169 \\ ARM Cortex-M55 (ARM Holdings (now a subsidiary of NVIDIA), Cambridge, UK) & 380–430 & 10–30 mW & €2.7–9.1 \\ TMS320C5505 (Texas Instruments, Dallas, TX, USA) & 200–300 & \textless{}10 mW & €9–11 \\ SmartHeaP (Institute of Microelectronic Systems, Leibniz University, Hannover, Germany) & 100 & \textless{}10 mW & — \\ \hline \hline \end{tabular}
\end{table}
Table 1: The MMACs, energy, and estimated cost of the DSP chips.  
architecture and lightweight techniques, recent speech enhancement models have progressively reduced both parameter size and computational burden. For instance, the GTCRN model [4] utilizes a grouped time convolution recurrent network architecture, incorporating ERB-based sub-band feature extraction and temporal recursive attention modules. It achieves a PESQ score of 2.87 on the VoiceBank-DEMAND dataset with only 23.7 K parameters and a computational cost of 33 MMACs. Samsung's FPSEN model [5] adopts a dual-channel structure combining full-band and sub-band features, enhanced by a Dual-Path Enhancer with Path Extension (DPE) to further optimize the ERB-based approach. This model attains a PESQ of 2.97 with 87 K parameters and 89 MMACs of computation, though its limited phase estimation capacity results in suboptimal performance in multi-speaker scenarios. LiSenNET [6] proposes a lightweight CRN architecture by retaining only the convolutional encoder and recurrent units, significantly reducing convolutional layers and RNN scale to improve computational efficiency. The model contains merely 56 K parameters and achieves a PESQ of 2.95. Although its performance is slightly lower than larger models, LiSenNET offers a feasible direction for subsequent lightweight model design.