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A Deep Generative Adversarial Method for Denoising Underwater Acoustic Signals

An underwater acoustic signal and depth technology, applied in the recognition of patterns in signals, neural learning methods, biological neural network models, etc., to achieve the effect of strong self-applicability, elimination of strong dependencies and over-fitting problems

Active Publication Date: 2022-06-28
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0005] In order to solve the small sample training problem unique to underwater acoustic signals, this paper proposes a deep generative confrontation method for underwater acoustic signal denoising technology

Method used

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  • A Deep Generative Adversarial Method for Denoising Underwater Acoustic Signals
  • A Deep Generative Adversarial Method for Denoising Underwater Acoustic Signals
  • A Deep Generative Adversarial Method for Denoising Underwater Acoustic Signals

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Embodiment Construction

[0037] Step 1 First, the samples are divided into frames and processed in batches.

[0038] Step 2 Then send the processed data into the generative model for model training. The generative model is a semi-supervised model, so there is a difference between the trained data and the clean data

[0039] Step 3: Add the data generated by the generator to the noisy data, and send it to the discriminant model together with the original clean and noisy data for discrimination. The discriminator can discriminate well at the beginning. The data of the generated model is a fake sample, and the output is 0. The original clean and noisy data is the real sample and the output is 1. According to the result of the discriminator, the generator starts to simulate its own generated data, so that the data is as close to the real data as possible, so that until the discriminator has no way to distinguish, the generator will generate The data is sent to the discriminator again, and the discriminato...

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Abstract

The invention discloses a depth generation confrontation method for underwater acoustic signal denoising, and belongs to the technical field of underwater acoustic signal denoising. In this method, the original underwater acoustic signal is first sampled and feature extracted, and then the extracted signal is sent to a Gauss-restricted Boltzmann machine for semi-supervised pre-training of the probability generation model; finally, a deep generation confrontation model is constructed, and the The data generated in the probability generation model and the real label data flow are sent to the Bernoulli restricted Boltzmann machine confrontation model for supervised training. Aiming at the feature extraction characteristics of the underwater acoustic signal, the present invention introduces a generative confrontation model into the restricted Boltzmann probability model, effectively eliminating the restricted Boltzmann machine during the training process caused by the complex signal carried by the underwater acoustic signal. The strong dependence and overfitting problems of the training model make the training model more self-applicable.

Description

technical field [0001] The invention belongs to the technical field of underwater acoustic signal noise reduction, and can effectively reproduce the original useful signal from the underwater acoustic signal with a large signal-to-noise ratio. Background technique [0002] In the existing underwater acoustic signal denoising, there are traditional denoising methods, modal decomposition method based on time domain and overall modal decomposition method based on frequency domain, which need to set some empirical parameters in advance, so that the denoising process depends on experience. The classical mode decomposition method can make the denoising process no longer need to set the function in advance, but it is easy to produce mode mixing and boundary effects in the decomposition process. In order to overcome boundary mixing, the underwater acoustic signal denoising method based on CEEMDAN, refined composite multi-scale dispersion entropy and wavelet threshold denoising has a...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F2218/04G06F2218/08G06F18/214
Inventor 曾向阳薛灵芝
Owner NORTHWESTERN POLYTECHNICAL UNIV
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