# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 1 Introduction  
A key technique in speech analysis and recognition, speech enhancement removes noises and undesired distortions to improve the quality of speech signals for enhanced communication. The technique can effectively eliminate or suppress unwanted acoustic components while preserving essential speech information, thereby improving the clarity and comprehensibility of speech. There have been many popular models for speech enhancement, including Wiener filtering [1], spectral subtraction [2], Kalman filtering [3], minimum mean square error estimation [4], and subspace approaches [5], but these traditional methods witness a significant decline in performance when the speech signals are contaminated by non-stationary noises or the signal-to-noise (SNR) ratio is low. With exceptional abilities in modeling nonlinear relationships, deep learning represents a viable solution to speech enhancement. Deep learning speech enhancement models can be broadly divided into two types--time-domain models and time-frequency (TF) domain models. The former focuses on capturing the temporal features of speech, in which the raw speech waveforms are used as both inputs and outputs; the latter takes into account not only the temporal but also the spectral features in speech signals, usingalgorithms like short-time Fourier transform (STFT) to convert the signals from the time domain to the frequency domain and produce spectrograms that contain magnitude and phase information at various time and frequency points. This information serves as both input and output. Despite their capacity to manage complex nonlinear noise, time-domain deep learning models frequently fall short of optimal performance. This shortcoming arises from their exclusive focus on temporal features, which prevents them from fully capturing spectral information and results in information loss. TF-domain deep learning approaches can be further categorized into two types--magnitude-domain models and complex-domain models: in the former, the spectra of clear speech are directly modeled, while the phases of noises are reconstructed to enhance the magnitude spectra; these enhanced magnitude spectra are then converted back into time-domain signals by the inverse short-time Fourier transform (ISTFT) method. One shortcoming of magnitude-domain models is that they overlook the phase information, which has been found to play an important role in enhancing the quality of speech [6]. Complex-domain models make up for this drawback as they consider both the magnitude and the phase information, but implementing these models can be very difficult as the phase components are random [7] and it is hard to estimate complex mask values [8]. After the emergence of the Transformer model [9], numerous studies applied it to various speech-processing tasks. One notable representative is SepFormer [10], which utilizes a dual-path transformer architecture to effectively capture both local and global contextual dependencies in the speech signal. The model's self-attention mechanism and dual-path structure contribute to its superior performance in improving separation quality and speech intelligibility compared to previous methods. However, the application scope of SepFormer primarily targets general speech separation tasks, and its design does not directly address the unique challenges of Air Traffic Control (ATC) communications, such as complex background noise, rapid speech rates, and channel distortions. The primary reason for this shortcoming is the lack of a direct correlation between the objective function and commonly used assessment indicators, which means that the assessment can fall short of expectations, even with an optimized objective loss function. To address this issue, [PERSON] et al.