Body: <p>Hi I want to create a neural network that essentially picks out the most pronounced parts of a spectrogram.</p>
  <p>Assume this is the True spectrogram:</p>
  <p><code>[0.0, 0.25, 0.95, 0.1, 0.0, ...]</code></p>
  <p>I want the auto encoder output to tend towards outputs like this:</p>
  <p><code>[0.0, 0.05, 1.0, 0.0, 0.0, ...]</code></p>
  <p>I essentially want to punish the neural network more for getting larger values more incorrect.</p>
  <p><code>True 0.2, Predicted 0.1</code></p>
  <p><code>True 0.9, Predicted 0.8</code> &lt;---- Far greater loss</p>
  <p>Since I don't care about background sounds this will make the important parts of the spectrogram more pronounced.</p>
  <blockquote>
  <p>Will this work? What should I use?</p>
  </blockquote>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-08-28T11:33:37.797Z
Comments.Text: From what you have posted, this looks like a task more easily solved with a simple filter.  Is there a reason you are preferring a NN?
Comments.UserDisplayName: David Hoelzer
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-08-28T11:42:34.347Z
Comments.Text: Because within the spectrogram there also random background noises that I add which will be removed @DavidHoelzer
Comments.UserDisplayName: GILO
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-08-29T00:48:15.463Z
Comments.Text: Why not do a simple SVD to remove the noise?
Comments.UserDisplayName: Joseph Turian
Tags: <tensorflow><autoencoders><unsupervised-learning><audio-processing>
Title: Autoencoder make spectrogram important parts more pronounced with a "log loss"
UserDisplayName: GILO
ThreadText: # Autoencoder make spectrogram important parts more pronounced with a "log loss"
  
  (Asked by: username_0 on 2022-08-27)
  
  Hi I want to create a neural network that essentially picks out the most pronounced parts of a spectrogram.
  
  Assume this is the True spectrogram:
  
  `[0.0, 0.25, 0.95, 0.1, 0.0, ...]`
  
  I want the auto encoder output to tend towards outputs like this:
  
  `[0.0, 0.05, 1.0, 0.0, 0.0, ...]`
  
  I essentially want to punish the neural network more for getting larger values more incorrect.
  
  `True 0.2, Predicted 0.1`
  
  `True 0.9, Predicted 0.8` <---- Far greater loss
  
  Since I don't care about background sounds this will make the important parts of the spectrogram more pronounced.
  
  > Will this work? What should I use?
  
  **Tags:** tensorflow, autoencoders, unsupervised-learning, audio-processing
  
  > **Comments:**
  > **username_1**: From what you have posted, this looks like a task more easily solved with a simple filter.  Is there a reason you are preferring a NN?  
  > **username_0**: Because within the spectrogram there also random background noises that I add which will be removed @username_1  
  > **username_2**: Why not do a simple SVD to remove the noise?  
  
  ---
  
  ## 0 answers
  
  