
Using deep learning to listen for whales - dnouri
http://danielnouri.org/notes/2014/01/10/using-deep-learning-to-listen-for-whales/
======
datawander
By the way, this is written by the sixth-place winner of last year's Kaggle
Competition for detecting Whales. [1]

I think another very interesting point brought up was when that even such a
well-ranked model on Kaggle did a poor job when applied to an different
dataset and had to be retained, which is a nice example of over-fitting.

Excellent article and nice details, thanks!

[1] [http://www.kaggle.com/c/whale-detection-
challenge/leaderboar...](http://www.kaggle.com/c/whale-detection-
challenge/leaderboard)

~~~
dnouri
And third-place winner of the second whale challenge :-)
[https://www.kaggle.com/c/the-icml-2013-whale-challenge-
right...](https://www.kaggle.com/c/the-icml-2013-whale-challenge-right-whale-
redux)

This second challenge actually featured a different dataset with different
hydrophones used etc. But even without retraining (which was rather trivial to
do at that point; the hard work of finding the right hyper parameters had
already been done), I would have still scored well above 90%. And I think Nick
Kridler reported the same.

So overfitting yes, but not too much considering there was a different sensor.

------
streptomycin
I have no idea if it's a common technique or not, but a couple years ago I met
some guys similarly using image analysis of spectrograms. They were trying to
diagnose sleep apnea based on of heart rate data. They had a company
developing a device and claimed to have patents on the technique, but I forget
the name. I just remember that it seemed like a convoluted algorithm to me,
and they agreed, but they claimed it worked better than any traditional
approach they tried.

~~~
dnouri
The nice thing about these convolutional neural nets is that they're not
convoluted at all. ;-) It's basically feed the raw data, in this case
spectrograms. Traditional approaches in this field are usually much more
convoluted, because they involve a complex feature extraction part. Which
tends to be specific to a certain species.

------
daviddumenil
Converting it to a spectrogram was a nice step.

From the perspective of other source data, I wonder if that limits you to five
features (X,Y and RGB) or whether you could extend to fictional/non-human-
visible colours as extra features and just be unable to view them in the
weight maps.

~~~
cdash
It mentions in the article that the spectrogram is really grayscale instead of
having RGB channels.

