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Image classification is a good demo/test case. However image sensors still cost multiple dollars, so one would likely spent a bit more in the microcontroller in that case. Accelerometer or microphone on the other hand adds just 30 cents to the BOM, and can be processed on similar cheap microcontroller. That is at least what I have found so far, trying to build a sub 1 dollar ML-powered system https://hackaday.io/project/194511-1-dollar-tinyml

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Great project! I used MNIST because it is easy to work with as a dataset. Audio classification would be quite interesting as a follow up, but I assume one would need some kind of transform to deal with the data in an easier way.

Thanks! Yeah transforming into a time-frequency is the standard method. Short Time Fourier Transform (STFT) using FFT is the most common, though one can use FIR/IIR filterbanks also. It is however quite challenging to do in just a few kB of RAM. It looks doable with 4 kB in total, miiight be possible with 2 kB.

Maybe something simpler, like a haar wavelet, would also work? Or DFT using Görtzel?

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