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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.

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    $\begingroup$ Tell us what you have found so far and why it's not helpful. Explain also exactly what you're looking for. $\endgroup$
    – nbro
    Commented Feb 13, 2022 at 11:12
  • $\begingroup$ @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. $\endgroup$ Commented Feb 21, 2022 at 19:11

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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.
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  • $\begingroup$ Interesting idea, do you know any articles that has done that on audio or any kind of time series data? $\endgroup$ Commented Feb 21, 2022 at 19:12
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    $\begingroup$ I haven't read any of these papers, but I found plenty from Google Scolar by searching for "autoencoder outlier detection". $\endgroup$
    – NikoNyrh
    Commented Feb 22, 2022 at 12:36

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