<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>

CC BY-SA 4.0
2022-08-28T11:33:37.797Z
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?
David Hoelzer
CC BY-SA 4.0
2022-08-28T11:42:34.347Z
Because within the spectrogram there also random background noises that I add which will be removed @DavidHoelzer
GILO
CC BY-SA 4.0
2022-08-29T00:48:15.463Z
Why not do a simple SVD to remove the noise?
Joseph Turian
<tensorflow><autoencoders><unsupervised-learning><audio-processing>
Autoencoder make spectrogram important parts more pronounced with a "log loss"
GILO
# 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 @DavidHoelzer  
> **username_2**: Why not do a simple SVD to remove the noise?  

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## 0 answers

