# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 3 Results and Analysis
### Results Analysis  
Our proposed model was compared with some recent and representative open-source models to verify its effectiveness in enhancing speech. For each noise reduction method, two to three models were chosen. Representative time-domain speech enhancement methods include SEGAN [21], TSTNN [22], and DEMUCS [23]. For time-frequency domain speech enhancement methods, two state-of-the-art models, PHASEN [24] and CMGAN, were evaluated. Among the models that utilize metric discriminators, MerticGAN+ [25] was selected as the most representative model for comparison. Table 1 presents their respective performance. According to the data presented in Table 1, the DeCGAN algorithm exhibits competitive performance across multiple evaluation metrics in the comparison. This suggests that DeCGAN effectively minimizes signal distortion during speech enhancement and excels in suppressing background noise. For instance, in comparison to the PHASEN model, which is designed with magnitude and phase awareness, DeCGAN improves the PESQ (by \(0.36\)), CSIG (by \(0.46\)), CBAK (by \(0.41\)), COVL (by \(0.55\)), and STOI scores. This is attributable to the ability of the mask and complex decoders to receive more accurate magnitude and phase spectra. When compared to the more advanced CMGAN model, DeCGAN achieves higher scores for CSIG, CBAK, and COVL, indicating that the DeConformer module, through the integration of TFC-SA and deformable convolution, can more accurately model all speech signals, both global and local. Such capability is particularly evident in its ability to restore detailed information and minimize noise interference. Although our DeCGAN model achieves slightly lower scores for PESQ and STOI scores in comparison with the CMGAN model, it still exhibits significant improvement in these metrics compared to other models. While CMGAN exhibits slightly higher PESQ scores, our DeCGAN model \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **PESQ** & **CSIG** & **CBAK** & **COVL** & **STOI** \\ \hline Noisy & 1.93 & 3.29 & 2.34 & 2.58 & 0.92 \\ SEGAN & 2.18 & 3.42 & 2.84 & 2.75 & 0.92 \\ TSTNN & 2.92 & 4.04 & 3.67 & 3.47 & 0.95 \\ DEMUCS & 3.03 & 4.25 & 3.3 & 3.58 & 0.95 \\ PHASEN & 2.95 & 4.15 & 3.45 & 3.57 & — \\ MetricGAN+ & 3.11 & 4.08 & 3.06 & 3.59 & — \\ CMGAN & **3.37** & 4.57 & 3.84 & 4.07 & 0.96 \\ DeCGAN & 3.31 & **4.61** & **3.86** & **4.12** & **0.96** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the evaluation metric scores of different models. The best results are in bold. achieves superior performance in other important metrics such as CSIG, CBAK, and COVID. This indicates that while CMGAN may slightly outperform in terms of perceptual quality as measured by PESQ, DeCGAN more effectively restores signal details and suppresses background noise, resulting in a more balanced overall enhancement performance. In addition, a segment of air traffic control speech test data was randomly selected for visual analysis, and the enhancement results of different models were compared. The time-domain waveform and spectrogram of the audio enhanced by various models are presented below. Specifically, the following representative models were selected for comparative analysis: MetricGAN+, which is based on a metric discriminator; DEMUCS, a representative model of time-domain speech enhancement; PHASEN, a model for time-frequency domain complex speech enhancement; and CMGAN, the baseline model for this algorithm. Through visual analysis, a direct comparison of the performance of different models in speech enhancement can be made. Figures 4 and 5 show the time-domain waveforms and spectrograms of speech signals enhanced by different models. Figures 4a and 5a depict the signals for noisy speech, where much more noise is present in the time-domain waveforms and the spectrograms in comparison to the clean speech signals, as depicted in Figures 4f and 5f. The noise was drawn from the test set, which included real ATC background noises that were not present during training.