Modulation Type Recognition Algorithm Based on Modulation Instantaneous Structure Difference and Deep Learning

Zhongfang, Wang; Zhai, Liang; Fu, Jiao

doi:10.1088/1755-1315/692/4/042073

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Wang et al put forward a new computing strategy, applying linguistic rules in the logarithmic posterior probability algorithm, and the correlation between machine scoring and manual scoring reached 0.795. Compared with the original algorithm, it is improved by 9% [ 16 ]. The evolution of speech-based machine learning has led to the development of a deep neural web, which is comparable to a neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Nonlinear Network Speech Recognition Structure in a Deep Learning Algorithm

Meng

Kuppuswamy

Upadhyay

et al. 2022

Computational Intelligence and Neuroscience

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As a result of the fast rise of globalization, people in China are learning English at a rapid pace. However, there is a severe shortage of English teachers in the region, which is a major hindrance. To address these concerns, a deep learning-based algorithm is proposed that can not only check English pronunciation but also help learners distinguish between phonemic and quality phonemic while listening and differentiating, as well as correct phonemic errors, thereby increasing their language learning capacity. In order to study the application of nonlinear network identification technology in English learning, this paper evaluates the English pronunciation quality through the deep learning algorithm of deep learning combined with the related contents of neural network data model, and the experimental results of speech recognition structure are analyzed and discussed in detail. The concordance between machine and manual intonation evaluation is 80%, the concordance rate of adjacent intonation evaluation is 98.33%, and the Pearson correlation coefficient is 0.627 that shows the technique is reliable. The method of English pronunciation and speech identification model is sensible and dependable, which can give beginners a punctual, exact and impartial judgment and response guidance, assist learners to get on the differences between their phonemic and standard phonemic, and correct phonemic mistakes, in order to enhance the ability of oral English learning.

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Section: Literature Reviewmentioning

confidence: 99%

Nonlinear Network Speech Recognition Structure in a Deep Learning Algorithm

Meng

Kuppuswamy

Upadhyay

et al. 2022

Computational Intelligence and Neuroscience

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“…The common features include instantaneous amplitude, phase-and frequency-features, high-order cumulant, high-order cumulant spectrum, and other high-order statistics; time-frequency features, such as the short-time Fourier transform and the wavelet transform; cyclic stationary features, such as the cyclic spectral density function and the cyclic spectral correlation function. The instantaneous amplitude, phase, and frequency features have poor anti-noise ability, and the effect is not good when used alone [5]. High-order statistics can effectively suppress the interference of Gaussian noise by taking advantage of the property that the high second-order cumulant of Gaussian noise is equal to zero [6].…”

Section: Introductionmentioning

confidence: 99%

Modulation Recognition of Communication Signals Based on Multimodal Feature Fusion

Zhang

Gong

et al. 2022

Sensors

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Modulation recognition is the indispensable part of signal interception analysis, which has always been the research hotspot in the field of radio communication. With the increasing complexity of the electromagnetic spectrum environment, interference in signal propagation becomes more and more serious. This paper proposes a modulation recognition scheme based on multimodal feature fusion, which attempts to improve the performance of modulation recognition under different channels. Firstly, different time- and frequency-domain features are extracted as the network input in the signal preprocessing stage. The residual shrinkage building unit with channel-wise thresholds (RSBU-CW) was used to construct deep convolutional neural networks to extract spatial features, which interact with time features extracted by LSTM in pairs to increase the diversity of the features. Finally, the PNN model was adapted to make the features extracted from the network cross-fused to enhance the complementarity between features. The simulation results indicated that the proposed scheme has better recognition performance than the existing feature fusion schemes, and it can also achieve good recognition performance in multipath fading channels. The test results of the public dataset, RadioML2018.01A, showed that recognition accuracy exceeds 95% when the signal-to-noise ratio (SNR) reaches 8 dB.

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Corpus Design of Chinese Medicine English Vocabulary Translation Teaching System Based on Python

Liu

2022

J. Info. Know. Mgmt.

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The current corpus has the problem of imperfect span retrieval function, which leads to a large classification noise. This paper designs a Python-based corpus of Chinese medicine English vocabulary translation teaching system. Here, we select the script material of web crawler, extract topic tags in the form of tag window, calculate the amount of information carried by words, use Python to extract the characteristics of Chinese medicine English vocabulary, and according to the observation value of exploration strategy, use instant time difference learning algorithm to construct the translation mode of teaching system, limit the scope of key words, and design the cross-range retrieval function of corpus. Experimental results: the average classification noise of the designed corpus and the other two corpora is 25.007[Formula: see text]dB, 33.877[Formula: see text]dB and 32.166[Formula: see text]dB, which proves that the integrated Python corpus has higher comprehensive value.

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Modulation Type Recognition Algorithm Based on Modulation Instantaneous Structure Difference and Deep Learning

Cited by 3 publications

References 5 publications

Nonlinear Network Speech Recognition Structure in a Deep Learning Algorithm

Nonlinear Network Speech Recognition Structure in a Deep Learning Algorithm

Modulation Recognition of Communication Signals Based on Multimodal Feature Fusion

Corpus Design of Chinese Medicine English Vocabulary Translation Teaching System Based on Python

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