## 1 Introduction  
In recent years, deep-neural-network-based (DNN-based) algorithms for speech enhancement have rapidly developed due to their advantages over traditional methods in dealing with non-stationary noise [1]. Speech is generally categorized into narrowband (sampling rate = 8 kHz), broadband (sampling rate = 16 kHz), ultra-wideband (sampling rate = 32 kHz), and full-band (sampling rate = 48 kHz) based on its sampling rate [2]. The aim of speech enhancement is to reduce the impact of environmental noise during the process of speech acquisition in various everyday scenarios, which can cause speech distortion and affect speech clarity and intelligibility [3]. Broadband speech contains the main energy and important harmonic information in human speech, thereby making it sufficient for better recovery of the speaker's speech and characteristics, and, thus, DNN-based speech enhancement primarily focuses on broadband speech [4]. However, full-band speech contains additional high-frequency information that can enhance the metallic sense of the speech, making it sound clearer, and is important for high-fidelity audio-visual communication [5]. As speech quality requirements continue to improve, full-band speech enhancement has gained more attention [6]. Current broadband speech enhancement algorithms cannot be directly applied to full-band speech enhancement [7]. Firstly, the spectrum dimension of full-band speech is three times that of broadband speech, leading to a higher calculation cost, which is unacceptable for real-time speech enhancement [8]. Secondly, the frequency band of full-band speech mainly consists of clear consonant components, thus resulting in relatively small speech energy and a low signal-to-noise ratio (SNR), which pose a challenge to speech enhancement. Thirdly, when using the model to process full-band speech, it is easy to focus on the high-frequency band with less energy, thereby leading to a decline in processing effectiveness of the more important low-frequency band [9]. Feature compression is a common method for processing full-band speech, and it involves transforming the Fourier spectrum into psychoacoustic excitation features for input. The Bark scale and equivalent rectangular bandwidth (ERB) scale are two frequently used methods for feature compression [10]. For instance, methods like RNNoise [11] and Personalized PercepNet [12] compress input features using the Bark scale and ERB scale, respectively. One approach employed by RNNoise was to use 22-dimensional Bark-frequency cepstral coefficients (BFCC) based on the Bark scale as input features, which were extracted from noisy speech using DSP techniques with 22 ideal critical band gains as the corresponding targets [11]. Another recent method, PercepNet, developed a perceptual band representation using 34 spectral bands based on the ERB scale, which is consistent with human hearing [12]. While these techniques have the potential to lower the dimensionality of frequency features and simplify calculations, they suffer from a decreased resolution of the spectrum in the Bark and ERB scales, which leads to imprecise spectrum reconstruction and a loss of information across frequency bands. Another approach is the finite impulse response (FIR)-based sub-band analysis and synthesis. This method has been applied to achieve high-quality results in text-to-speech (TTS) tasks, as seen in the success of Multi-band WaveRNN [13] and Multi-band MelGAN [14], which have received high mean opinion scores (MOS). The advantage of this approach is that it can achieve more accurate frequency analysis and processing, as well as improve the effectiveness of speech enhancement and sound quality. The full-band speech is divided into multiple sub-bands for analysis and processing, and different FIR filters can be designed for each sub-band to meet the enhancement requirements in different frequency ranges. FIR filters have the advantages of linear phase and stability, which can effectively avoid the problems of signal distortion and filter instability [15].