I was struck by the comparison between audio spectra and image spectra. Image spectra have a strong power law effect, but audio spectra have more power in middl

I was struck by the comparison between audio spectra and image spectra. Image spectra have a strong power law effect, but audio spectra have more power in middle bands. Why? One part of the issue is that the visual spectrum is very narrow (just 1 order of magnitude from red to blue) compared to audio (4 orders of magnitude from 20Hz to 20kHz). But another issue not mentioned in the article is that in images we can zoom in/out arbitrarily. So the width of a pixel can change – it might be 1mm in one image, or 1cm in another, or 1m or 1km. Whereas in audio, the “width of a pixel” (the time between two audio samples) is a fixed amount of time – usually 1/44.1kHz, but even if it’s at a different sample rate, we would convert all images to have the same sample rate before training an NN. The equivalent of this for images would be rescaling all images so that a picture of a cat is say 100x100 pixels, while a picture of a tiger is 300x300. Which, come to think of it, would be potentially an interesting thing to do.