{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:19:07Z","timestamp":1753881547458,"version":"3.41.2"},"reference-count":26,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":315,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Construction of First-Class Disciplines in Ningxia Colleges and Universities","award":["NXYLXK2017B11"],"award-info":[{"award-number":["NXYLXK2017B11"]}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:p>With the development of information technology, band expansion technology is gradually applied to college English listening teaching. This technology aims to recover broadband speech signals from narrowband speech signals with a limited frequency band. However, due to the limitations of current voice equipment and channel conditions, the existing voice band expansion technology often ignores the high\u2010frequency and low\u2010frequency correlation of the audio, resulting in excessive smoothing of the recovered high\u2010frequency spectrum, too dull subjective hearing, and insufficient expression ability. In order to solve this problem, a neural network model PCA\u2010NN (principal components analysis\u2010neural network) based on principal component image analysis is proposed. Based on the nonlinear characteristics of the audio image signal, the model reduces the dimension of high\u2010dimensional data and realizes the effective recovery of the high\u2010frequency detailed spectrum of audio signal in phase space. The results show that the PCA\u2010NN, i.e., neural network based on principal component analysis, is superior to other audio expansion algorithms in subjective and objective evaluation; in log spectrum distortion evaluation, PCA\u2010NN algorithm obtains smaller LSD. Compared with EHBE, Le, and La, the average LSD decreased by 2.286\u2009dB, 0.51\u2009dB, and 0.15\u2009dB, respectively. The above results show that in the image frequency band expansion of college English listening, the neural network algorithm based on principal component analysis (PCA\u2010NN) can obtain better high\u2010frequency reconstruction accuracy and effectively improve the audio quality.<\/jats:p>","DOI":"10.1155\/2021\/9732156","type":"journal-article","created":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T05:20:07Z","timestamp":1636780807000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English 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