# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 1 Introduction
proposed MetricGAN [11], a time-frequency domain speech enhancement model that leverages a discriminator to determine the evaluation metric function in order to facilitate more direct optimization of speech quality and intelligibility metrics, resulting in generated speech that aligns more closely with human auditory perception. [PERSON] et al. [12] introduced CMGAN, a Conformer-based MetricGAN model. Similar to MetricGAN, CMGAN employs an evaluation metric-based optimization strategy within its metric discriminator, while integrating the advantages of the Conformer model. Specifically, in the time and frequency domains, the CMGAN adopts a dual-stage Conformer architecture to capture long-term dependencies and local features. However, since multi-scale and stage-wise modeling is employed in the CMGAN for the time and frequency domains, the direct correlation between the temporal and spectral information is considerably diminished. Furthermore, the lack of explicit modeling in the channel dimension restricts its ability to effectively capture global information. Besides speech enhancement, recent research such as the Res-NeXt-Mssm-CTC [13] has shown satisfactory results in automatic speech recognition (ASR) for air traffic control. The Res-NeXt-Mssm-CTC mainly focuses on optimizing transcription accuracy directly. However, the applications of speech enhancement are broader than that of speech recognition in air traffic control scenarios. On the one hand, speech enhancement directly improves the clarity and intelligibility of communications between pilots and controllers. On the other hand, while speech recognition converts speech to text for transcription, its performance is highly dependent on the quality of the input signal. Speech enhancement can serve as a front-end pre-processing step, benefiting not only ASR systems but also other applications such as speaker verification, emotion detection, and situational awareness systems in air traffic control. Therefore, to meet this need, we propose a novel speech enhancement model to denoise speech in the air traffic control domain. Given the reviews above, DeCGAN, a MetricGAN model based on the DeConformer architecture, is put forth in the present work to address speech enhancement. At the core of the DeCGAN model is the combination of a generator and a metric discriminator: the former, unlike conventional dual-branch models, which are typically designed separately for magnitude masking and complex spectrum optimization, depends on a shared decoder to process concatenated magnitude information and complex (be it real or imaginary) components as inputs; the latter excels in estimating and optimizing a black-box nondifferentiable metric without adversely affecting other metrics. Unlike conventional dual-branch models, which are typically designed separately for magnitude masking and complex spectrum optimization, the generator in this work employs a shared encoder that processes the concatenated magnitude and complex (real and imaginary) components as input. Meanwhile, to address the limitations of traditional Conformer models, our DeCGAN has a DeConformer module in which the Time Frequency Channel Attention (TFC-SA) and Deformable Convolution-based Feedforward Neural Network (DeConv-FNN) modules are combined to model long-term dependencies across the dimensions of time, frequency, and channel. This design facilitates more efficient local feature processing and enhances the sequential extraction of temporal and spectral information from the encoder. Subsequently, the architecture splits into a dedicated mask decoder for magnitude interpretation, along with another branch to refine both real and imaginary facets. The main contributions of our methods are as follows:  
* This paper proposes DeCGAN, a generator based on DeConformer that integrates TFC-SA and DeConv-FFN, enabling the simultaneous capture of global long-range dependencies and local fine-grained details in speech signals.