Answers.Body: <p>So , you want to remove the "noise" (damaged signal) from the signal, by giving the perfect signal as output. This model is called as a "denoising autoencoder". If you have enough training data, the noise is not at all a problem for the neural network , if you really feel that the network is capturing the noise , then you have to increase the training epochs , or sometimes it can be a bad initialized network. </p>
  
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Answers.Text: Hi, thanks for your answer. The differences between the damaged signal and the training signal do not come from noise, but are due to a nonlinear operation which acts only on certain areas. Though it is not this case, an amplitude limiter would be a good example of what I am trying to deal with.
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Body: <p>I am starting to study the capabilities of neural networks for the reconstruction/restoration/... of communication signals.</p>
  
  <p>I am feeding my neural network with a signal which has some parts which have been damaged because of the transmission through a communication system, and my targets are given by the signal with these areas undamaged. </p>
  
  <p>The problem is that the areas damaged represent a very small portion of the whole signal, and my neural network spends lot of time learning only from the portions which actually do not present any problem.</p>
  
  <p>Is there any solution to make the neural network to jump on those areas which show significant differences to respect to the targets? Is there anything I could do for example initializing my neural networks (as conventionally done, they are initialized randomly)? Or shall I accept that I need to train for longer time?</p>
  
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Comments.CreationDate: 2018-07-02T16:05:27.153Z
Comments.Text: Can you give more details about the network , in particular , the architechture , learning rate used , etc
Comments.UserDisplayName: thecomplexitytheorist
Tags: <neural-networks><training>
Title: Restoration of localized damaged areas (time signals, but guess also applicable to images)
UserDisplayName: user1113242
ThreadText: # Restoration of localized damaged areas (time signals, but guess also applicable to images)
  
  (Asked by: username_0 on 2018-07-02)
  
  I am starting to study the capabilities of neural networks for the reconstruction/restoration/... of communication signals.
  
  I am feeding my neural network with a signal which has some parts which have been damaged because of the transmission through a communication system, and my targets are given by the signal with these areas undamaged.
  
  The problem is that the areas damaged represent a very small portion of the whole signal, and my neural network spends lot of time learning only from the portions which actually do not present any problem.
  
  Is there any solution to make the neural network to jump on those areas which show significant differences to respect to the targets? Is there anything I could do for example initializing my neural networks (as conventionally done, they are initialized randomly)? Or shall I accept that I need to train for longer time?
  
  **Tags:** neural-networks, training
  > **Comments:**
  > **username_1**: Can you give more details about the network , in particular , the architechture , learning rate used , etc  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2018-07-02. Score: 1
  
  So , you want to remove the "noise" (damaged signal) from the signal, by giving the perfect signal as output. This model is called as a "denoising autoencoder". If you have enough training data, the noise is not at all a problem for the neural network , if you really feel that the network is capturing the noise , then you have to increase the training epochs , or sometimes it can be a bad initialized network.
  
  > **Comments:**
  > **username_0**: Hi, thanks for your answer. The differences between the damaged signal and the training signal do not come from noise, but are due to a nonlinear operation which acts only on certain areas. Though it is not this case, an amplitude limiter would be a good example of what I am trying to deal with.  
  
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