## 1 Introduction
However, the design and optimization of FIR filters require certain experience and skills, which may increase the difficulty of algorithm development and debugging. The third approach, spectrum splitting, is a popular approach for speech enhancement research. There are two recent models [16; 17] that apply spectrum splitting, rather than directly modeling the full-band feature, after performing a short-time Fourier transform (STFT). DMF-Net [18] and SF-Net [19] use a cascaded structure for spectrum splitting. These methods divide full-band speech into several continuous sub-bands and then analyze and process each sub-band. DMF-Net proposes a new deep-learning model for full-band speech enhancement that uses a decoupling-style multi-band fusion approach. SF-Net adopts a coordinated strategy when fusing sub-bands to ensure better collaboration between different sub-bands. This method does not require the design and operation of complex FIR filters, so the calculation cost and memory occupation are relatively low. In addition, this method can avoid overlap and information loss between sub-bands, thereby leading to an improved speech enhancement effect and quality through reasonable frequency band division. Some recent full-band speech processing algorithms can be found in Table 1.  
In previous work, we proposed the CGA-MGAN [20], a convolution-augmented gated attention metric GAN for speech enhancement. By constructing convolution-augmented gated attention units (CGAU), the network can capture time and frequency dependence with lower computational complexity and obtain better speech enhancement results. In addition, the CGA-MGAN constructs an encoder-decoder structure including gating blocks \begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Algorithm** \\ \hline Feature Compression & RNNoise, PercepNet \\ Finite Impulse Response & Multi-band WaveRNN, Multi-band MelGAN \\ Spectrum Splitting & DMF-Net, SF-Net \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Recent full-band speech processing algorithms. using the decoupling-style phase-aware method, which can collaboratively estimate clean speech's magnitude and phase information in parallel and avoid compensatory effects between the two. However, the encoder-decoder structure uses triple sub-decoders to process magnitude, real part, and imaginary part information, respectively, and there is no parameter interaction between them, which limits its speech enhancement ability. In this paper, we propose a two-stage complex and real dual-path sub-band fusion network, TS-CGANet, for full-band speech enhancement. Specifically inspired by SF-Net, we divided the original full-band spectrum into three frequency bands--a 0-8 kHz low band (LB), an 8-16 kHz medium band (MB), and a 16-24 kHz high band (HB)--and designed three subnetworks to respectively process them. First, we designed a CGA-based complex and real dual-path speech enhancement network, LB-Net, to enhance LB speech. It uses a decoupling-style phase-aware method to avoid the influence of the compensation effect. Subsequently, we built the MB-Net and HB-Net to enhance the speech of MB and HB. MB-Net and HB-Net only enhance the magnitude of speech in the high-band and keep the phase unchanged. In addition, when using MB-Net and HB-Net for speech enhancement, the pretreated LB-Net will provide additional guidance. Finally, the frequency bands processed by the three networks were fused to recover the full-band speech. We outline our main contributions as follows:  
* We introduced the CGAU into full-band speech enhancement and used spectrum splitting to process full-band speech.