<p>Yes, Convolutional Neural Networks(CNNs) can and have been applied to non-image problems. Arguably, any problem in which the location of a feature(s) is relevant can be attempted via CNNs. CNNs works under the assumption that points close to each other in the data share some correlations/relationship whist points further apart don't share as much information. So, theoretically, if you can phrase your problem so that it meets this requirement(s), it can be attempted by a convolutional neural network. Here are few applications of CNNss that are dont involve images: </p>

<ul>
<li><a href="http://ieeexplore.ieee.org/document/6857341/?reload=true" rel="noreferrer">Speech recognition</a></li>
<li><a href="https://arxiv.org/abs/1509.01626" rel="noreferrer">Character-level Convolutional Networks for Text Classification</a></li>
<li><a href="https://chara.cs.illinois.edu/sites/sp16-cs591txt/files/0226-presentation.pdf" rel="noreferrer">Text classification using CNNs</a></li>
<li><a href="http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/" rel="noreferrer">Natural Language Processing</a></li>
<li><a href="https://arxiv.org/pdf/1703.04691.pdf" rel="noreferrer">Time Series Forecasting</a></li>
<li><a href="https://arxiv.org/abs/1404.2188" rel="noreferrer">Sentence modeling</a></li>
<li><p>I've used 1-D CNNs on spectral data. Here are some examples of <a href="https://arxiv.org/abs/1710.01927" rel="noreferrer">CNNs applied to spectral data</a> : </p>

<ul>
<li><a href="https://pdfs.semanticscholar.org/2af4/f72c2326c15616ad9f5cf18d5278d6608a14.pdf" rel="noreferrer">Spectral Convolutional Neural Network for music classification</a></li>
<li><a href="http://ieeexplore.ieee.org/document/7730323/" rel="noreferrer">Convolutional neural network based classification for hyperspectral data</a></li>
</ul></li>
</ul>

CC BY-SA 4.0
2017-11-06T11:11:04.660Z
2018-07-22T02:15:29.647Z
2018-07-22T02:15:29.647Z
Tshilidzi Mudau
<p>CNNs are generally designed for 2D (generally image) data. Hence, other usages are most likely a "hack" to CNN logic. </p>

<p>You can check how to extract features and classify texts with CNN through <a href="http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/" rel="nofollow noreferrer">here</a>.</p>

CC BY-SA 3.0
2017-11-06T06:10:29.393Z
2017-11-06T06:10:29.393Z
Deniz Beker
<p>I'm new in this argument, my question is:</p>

<p>Can convolution be applied in other contexts different from image recognition?
Is there a good source to learn from?</p>

<neural-networks><convolutional-neural-networks>
Can Convolutional Neural Networks be applied in domains other than image recognition?
Francesco Rizzi
# Can Convolutional Neural Networks be applied in domains other than image recognition?

(Asked by: username_0 on 2017-11-05)

I'm new in this argument, my question is:

Can convolution be applied in other contexts different from image recognition?
Is there a good source to learn from?

**Tags:** neural-networks, convolutional-neural-networks


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## 2 answers

### Answer by username_1 on 2017-11-06. Score: 8

Yes, Convolutional Neural Networks(CNNs) can and have been applied to non-image problems. Arguably, any problem in which the location of a feature(s) is relevant can be attempted via CNNs. CNNs works under the assumption that points close to each other in the data share some correlations/relationship whist points further apart don't share as much information. So, theoretically, if you can phrase your problem so that it meets this requirement(s), it can be attempted by a convolutional neural network. Here are few applications of CNNss that are dont involve images:

* [Speech recognition](http://ieeexplore.ieee.org/document/6857341/?reload=true)
* [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626)
* [Text classification using CNNs](https://chara.cs.illinois.edu/sites/sp16-cs591txt/files/0226-presentation.pdf)
* [Natural Language Processing](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/)
* [Time Series Forecasting](https://arxiv.org/pdf/1703.04691.pdf)
* [Sentence modeling](https://arxiv.org/abs/1404.2188)
* I've used 1-D CNNs on spectral data. Here are some examples of [CNNs applied to spectral data](https://arxiv.org/abs/1710.01927) :

  + [Spectral Convolutional Neural Network for music classification](https://pdfs.semanticscholar.org/2af4/f72c2326c15616ad9f5cf18d5278d6608a14.pdf)
  + [Convolutional neural network based classification for hyperspectral data](http://ieeexplore.ieee.org/document/7730323/)


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### Answer by username_2 on 2017-11-06. Score: 0

CNNs are generally designed for 2D (generally image) data. Hence, other usages are most likely a "hack" to CNN logic.

You can check how to extract features and classify texts with CNN through [here](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/).


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