Neural network for calculating direct and inverse nonlinear Fourier transform

Sedov, Egor; Chekhovskoy, I. S.; Prilepsky, Jaroslaw E.

doi:10.1070/qel17655

Cited by 5 publications

(4 citation statements)

References 36 publications

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“…denotes the center of the power spectrum of the h th mode; ( ) ˆh  denotes the Wiener filter of the current residual. The VMD algorithm continuously updates each mode in the frequency domain, and then converts it to the time domain by using the nonlinear Fourier transform [10]. The steps of updating the mode components are as follows:…”

Section: Signal Decomposition Based On Variational Modal Decompositio...mentioning

confidence: 99%

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Application of Deep Neural Network Algorithm in Speech Enhancement of Online English Learning Platform

Peng

Zhang

2022

EAI Endorsed Scal Inf Syst

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INTRODUCTION: In the online English learning platform, noise interference makes people unable to hear the content of English teaching clearly, which leads to a great reduction in the efficiency of English learning. In order to improve the voice quality of online English learning platform, the speech enhancement method of the online English learning platform based on deep neural network is studied.OBJECTIVES: This paper proposes a deep neural network-based speech enhancement method for online English learning platform in order to obtain more desirable results in the application of speech quality optimization.METHODS: The optimized VMD (Variable Modal Decomposition) algorithm is combined with the Moth-flame optimization algorithm to find the optimal solution to obtain the optimal value of the decomposition mode number and the penalty factor of the variational modal decomposition algorithm, and then the optimized variational modal decomposition algorithm is used to filter the noise information in the speech signal; Through the network speech enhancement method based on deep neural network learning, the denoised speech signal is taken as the enhancement target to achieve speech enhancement.RESULTS: The research results show that the method not only has significant denoising ability for speech signal, but also after this method is used, PESQ value of speech quality perception evaluation of speech signal is greater than 4.0dB, the spectral features are prominent, and the speech quality is improved.CONCLUSION: Through experiments from three perspectives: speech signal denoising, speech quality enhancement and speech spectrum information, the usability of the method in this paper is confirmed.

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Section: Signal Decomposition Based On Variational Modal Decompositio...mentioning

confidence: 99%

“…( ) ( ) (10) where j E denotes the distance between the j th moth and the i th flame; c is the defined logarithmic spiral shape constant and the path coefficient…”

Section: Optimization Of Vmd Algorithm Based On Moth-flame Optimizati...mentioning

confidence: 99%

Application of Deep Neural Network Algorithm in Speech Enhancement of Online English Learning Platform

Peng

Zhang

2022

EAI Endorsed Scal Inf Syst

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“…Furthermore, the previous work only demonstrated networks for a fixed signal power without considering fibre loss and in the case of lump amplification. While this paper was being reviewed, two new articles were published 27 , 28 . Both of them were aimed at obtaining the nonlinear spectrum of a signal.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the training data does not include variation in signal propagation distance, absorption loss and variation in power, hence the generality is hard to evaluate. In the second work 28 , a network architecture similar to the one in the work 29 is used for both direct and inverse NFT. However, this article did not give any details on the training data.…”

Section: Introductionmentioning

confidence: 99%

Serial and parallel convolutional neural network schemes for NFDM signals

Zhang

Chan

Vahid

2022

Sci Rep

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Two conceptual convolutional neural network (CNN) schemes are proposed, developed and analysed for directly decoding nonlinear frequency division multiplexing (NFDM) signals with hardware implementation taken into consideration. A serial network scheme with a small network size is designed for small user applications, and a parallel network scheme with high speed is designed for places such as data centres. The work aimed at showing the potential of using CNN for practical NFDM-based fibre optic communication. In the numerical demonstrations, the serial network only occupies 0.5 MB of memory space while the parallel network occupies 128 MB of memory but allows parallel computing. Both network schemes were trained with simulated data and reached more than 99.9% accuracy.

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