{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:31:28Z","timestamp":1778167888491,"version":"3.51.4"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T00:00:00Z","timestamp":1745452800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Administration of China","award":["F2024KF01A"],"award-info":[{"award-number":["F2024KF01A"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Air traffic control (ATC) communication is susceptible to speech noise interference, which undermines the quality of civil aviation speech. To resolve this problem, we propose a speech enhancement model, termed DeCGAN, based on the DeConformer generative adversarial network. The model\u2019s generator, the DeConformer module, combining a time frequency channel attention (TFC-SA) module and a deformable convolution-based feedforward neural network (DeConv-FFN), effectively captures both long-range dependencies and local features of speech signals. For this study, the outputs from two branches\u2014the mask decoder and the complex decoder\u2014were amalgamated to produce an enhanced speech signal. An evaluation metric discriminator was then utilized to derive speech quality evaluation scores, and adversarial training was implemented to generate higher-quality speech. Subsequently, experiments were performed to compare DeCGAN with other speech enhancement models on the ATC dataset. The experimental results demonstrate that the proposed model is highly competitive compared to existing models. Specifically, the DeCGAN model achieved a perceptual evaluation of speech quality (PESQ) score of 3.31 and short-time objective intelligibility (STOI) value of 0.96.<\/jats:p>","DOI":"10.3390\/a18050245","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T09:26:58Z","timestamp":1745486818000},"page":"245","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DeCGAN: Speech Enhancement Algorithm for Air Traffic Control"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8293-3888","authenticated-orcid":false,"given":"Haijun","family":"Liang","sequence":"first","affiliation":[{"name":"Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Jianyang 641400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8956-1495","authenticated-orcid":false,"given":"Yimin","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Jianyang 641400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanwen","family":"Chang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Jianyang 641400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianguo","family":"Kong","sequence":"additional","affiliation":[{"name":"Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Flight University of China, Jianyang 641400, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1109\/TASSP.1978.1163086","article-title":"All-pole modeling of degraded speech","volume":"26","author":"Lim","year":"1978","journal-title":"IEEE Trans. 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