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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.

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.

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