Abstract
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.
Highlights
English listening test is an important part of the English proficiency test and plays an important role in English proficiency learning
Due to the limitations of the existing voice acquisition equipment, voice playback equipment, and communication conditions, there are some problems in the English listening audio, such as poor sound quality and natural damage, which have a great impact on the learning and evaluation of English listening [1]. erefore, how to improve the sound quality of English listening through existing technical means has attracted more and more researchers’ attention
The frequency band method based on neural network can effectively predict and recover the high-frequency spectrum envelope and spectrum details, the existing models often use the methods of spectrum moving and spectrum folding to recover the detailed spectrum, ignore the correlation between high and low frequency of the audio, do not follow the evolution law of audio signal itself, and ignore the research on the nonlinear system generating the audio signal. is leads to spectral distortion of the recovered audio signal
Summary
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. E 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. E 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
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