Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening

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 voic...

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Autor principal:	Cailing Hao
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/c42b83d3b6aa483f9d6f6775d441ae5d
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spelling	oai:doaj.org-article:c42b83d3b6aa483f9d6f6775d441ae5d2021-11-22T01:11:23ZApplication of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening1687-527310.1155/2021/9732156https://doaj.org/article/c42b83d3b6aa483f9d6f6775d441ae5d2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9732156https://doaj.org/toc/1687-5273With 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-frequency and low-frequency correlation of the audio, resulting in excessive smoothing of the recovered high-frequency spectrum, too dull subjective hearing, and insufficient expression ability. In order to solve this problem, a neural network model PCA-NN (principal components analysis-neural 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-dimensional data and realizes the effective recovery of the high-frequency detailed spectrum of audio signal in phase space. The results show that the PCA-NN, 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-NN algorithm obtains smaller LSD. Compared with EHBE, Le, and La, the average LSD decreased by 2.286 dB, 0.51 dB, and 0.15 dB, 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-NN) can obtain better high-frequency reconstruction accuracy and effectively improve the audio quality.Cailing HaoHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7Neurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENComputational Intelligence and Neuroscience, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Cailing Hao Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening
description	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-frequency and low-frequency correlation of the audio, resulting in excessive smoothing of the recovered high-frequency spectrum, too dull subjective hearing, and insufficient expression ability. In order to solve this problem, a neural network model PCA-NN (principal components analysis-neural 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-dimensional data and realizes the effective recovery of the high-frequency detailed spectrum of audio signal in phase space. The results show that the PCA-NN, 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-NN algorithm obtains smaller LSD. Compared with EHBE, Le, and La, the average LSD decreased by 2.286 dB, 0.51 dB, and 0.15 dB, 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-NN) can obtain better high-frequency reconstruction accuracy and effectively improve the audio quality.
format	article
author	Cailing Hao
author_facet	Cailing Hao
author_sort	Cailing Hao
title	Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening
title_short	Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening
title_full	Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening
title_fullStr	Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening
title_full_unstemmed	Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening
title_sort	application of neural network algorithm based on principal component image analysis in band expansion of college english listening
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/c42b83d3b6aa483f9d6f6775d441ae5d
work_keys_str_mv	AT cailinghao applicationofneuralnetworkalgorithmbasedonprincipalcomponentimageanalysisinbandexpansionofcollegeenglishlistening
_version_	1718418305731002368

Application of Neural Network Algorithm Based on Principal Component Image Analysis in Band Expansion of College English Listening

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