<p>Here's what i have so far.</p>
<p>Pros:</p>
<ul>
<li>no extra labeling required</li>
<li>some software libraries make it very easy for you these days. See muda, specaugment, audiomentations</li>
<li>you can listen to the result to check if you're doing things right (if you can't recognize the sound after augmentation, it means it's too harsh and you should reparametrize it so it retains recognizability)</li>
<li>at the end of the day, it's the same model architecture, just trained differently. so the time/memory budget at prediction time is the same.</li>
<li>it almost always improves performance, and it's easy to understand why</li>
</ul>
<p>Cons:</p>
<ul>
<li>the task is more difficult, so convergence is slower at training time, so you need to iterate more through the augmented dataset than through the original dataset, so you're going need to plan for a longer training time</li>
<li>be careful not to go overboard with it. as i wrote above, if your data augmentation is so harsh that it jeopardizes the recognizability of the sound, it won't work</li>
</ul>
<p>Additional answers/comments are welcome :)</p>

CC BY-SA 4.0
2022-08-07T07:17:11.103Z
2022-08-07T07:17:45.137Z
2022-08-07T07:17:45.137Z
lostanlen
<p>Even though we already have a discussion regarding what kind of data augmentation makes sense for ML in bioacoustics (<a href="https://bioacoustics.stackexchange.com/questions/98/data-augmentation-strategies-for-bioacoustics-machine-learning">Data augmentation strategies for bioacoustics machine learning</a>), i believe it would be helpful to list out some pros and cons of using data augmentation at all?</p>
<p>(initially asked by Julie Oswald at the Full-stack Bioacoustics workshop in Leyden)</p>

CC BY-SA 4.0
2022-08-07T08:27:33.053Z
Hi Iostanlen, welcome to stack exchange! To make your question more different from the existing question you mentioned, what about focusing on the cons only? I have the impression that the advantages are already given in the mentioned thread, aren't they?
Noil
CC BY-SA 4.0
2022-08-07T18:17:14.080Z
the mentioned question asks about the how. i'm asking about the why (and why not).
lostanlen
CC BY-SA 4.0
2022-08-07T21:11:13.230Z
Oh okk! Sorry for the misguidance!
Noil
<machine-learning><signal-processing><data-augmentation>
Data augmentation in bioacoustics: pros and cons?
# Data augmentation in bioacoustics: pros and cons?

(Asked by: username_0 on 2022-08-07)

Even though we already have a discussion regarding what kind of data augmentation makes sense for ML in bioacoustics ([Data augmentation strategies for bioacoustics machine learning](https://bioacoustics.stackexchange.com/questions/98/data-augmentation-strategies-for-bioacoustics-machine-learning)), i believe it would be helpful to list out some pros and cons of using data augmentation at all?

(initially asked by Julie Oswald at the Full-stack Bioacoustics workshop in Leyden)

**Tags:** machine-learning, signal-processing, data-augmentation

> **Comments:**
> **username_1**: Hi Iostanlen, welcome to stack exchange! To make your question more different from the existing question you mentioned, what about focusing on the cons only? I have the impression that the advantages are already given in the mentioned thread, aren't they?  
> **username_0**: the mentioned question asks about the how. i'm asking about the why (and why not).  
> **username_1**: Oh okk! Sorry for the misguidance!  

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## 1 answers

### Answer by username_0 on 2022-08-07. Score: 4 | Accepted answer

Here's what i have so far.

Pros:

* no extra labeling required
* some software libraries make it very easy for you these days. See muda, specaugment, audiomentations
* you can listen to the result to check if you're doing things right (if you can't recognize the sound after augmentation, it means it's too harsh and you should reparametrize it so it retains recognizability)
* at the end of the day, it's the same model architecture, just trained differently. so the time/memory budget at prediction time is the same.
* it almost always improves performance, and it's easy to understand why

Cons:

* the task is more difficult, so convergence is slower at training time, so you need to iterate more through the augmented dataset than through the original dataset, so you're going need to plan for a longer training time
* be careful not to go overboard with it. as i wrote above, if your data augmentation is so harsh that it jeopardizes the recognizability of the sound, it won't work

Additional answers/comments are welcome :)


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