<p>A first think what comes to mind is to train an autoencoder, then identify abnormal data by these heuristics:</p>
<ul>
<li>Is the reconstruction error large, for example remove the top 5% of the data?</li>
<li>Are the codes (outputs of the encoder) within a densely populated region, or are they outliers? You could calculate the distance to Nth nearest neighbor.</li>
</ul>

CC BY-SA 4.0
2022-02-21T19:12:49.537Z
Interesting idea, do you know any articles that has done that on audio or any kind of time series data?
Sepehr Golestanian
CC BY-SA 4.0
2022-02-22T12:36:38.653Z
I haven't read any of these papers, but I found plenty from Google Scolar by searching for "autoencoder outlier detection".
NikoNyrh
CC BY-SA 4.0
2022-02-13T11:53:08.353Z
2022-02-13T11:53:08.353Z
NikoNyrh
<p>I'm currently working on an audio classification project using CNNs. The problem is I'm having trouble training my CNN. I doubt if there are outliers in my dataset but I don't know how to detect outliers in an <strong>audio dataset</strong>. I've searched google and found nothing helpful.</p>

CC BY-SA 4.0
2022-02-13T11:12:17.547Z
Tell us what you have found so far and why it's not helpful. Explain also exactly what you're looking for.
nbro
CC BY-SA 4.0
2022-02-21T19:11:09.967Z
@nbro almost everything I found on google was about finding outliers in tabular data, therefore, they're not applicable to audio or any kind of time series data.
Sepehr Golestanian
<data-preprocessing>
how to detect ouliers in audio dataset?
Sepehr Golestanian
# how to detect ouliers in audio dataset?

(Asked by: username_0 on 2022-02-13)

I'm currently working on an audio classification project using CNNs. The problem is I'm having trouble training my CNN. I doubt if there are outliers in my dataset but I don't know how to detect outliers in an **audio dataset**. I've searched google and found nothing helpful.

**Tags:** data-preprocessing

> **Comments:**
> **username_1**: Tell us what you have found so far and why it's not helpful. Explain also exactly what you're looking for.  
> **username_0**: @username_1 almost everything I found on google was about finding outliers in tabular data, therefore, they're not applicable to audio or any kind of time series data.  

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## 1 answers

### Answer by username_2 on 2022-02-13. Score: 1

A first think what comes to mind is to train an autoencoder, then identify abnormal data by these heuristics:

* Is the reconstruction error large, for example remove the top 5% of the data?
* Are the codes (outputs of the encoder) within a densely populated region, or are they outliers? You could calculate the distance to Nth nearest neighbor.

> **Comments:**
> **username_0**: Interesting idea, do you know any articles that has done that on audio or any kind of time series data?  
> **username_2**: I haven't read any of these papers, but I found plenty from Google Scolar by searching for "autoencoder outlier detection".  

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