Depth generative adversarial method for underwater acoustic signal denoising
An underwater acoustic signal and depth technology, which is applied in the recognition of patterns in signals, neural learning methods, biological neural network models, etc. The effect of fitting the problem
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[0037] In step 1, the samples are first 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. At the beginning, the discriminator can discriminate well. The data generated by the model is a fake sample, and the output is 0. Originally, the clean and noisy data is a 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 as possible to the real data, so that until the discriminator has no way to distinguish, the generator will generate The data is sent to the discriminator again, and the discrim...
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