3 ms·
I don't think I can agree with that. You do a Fourier transform when the data you're working with doesn't admit easily or at all certain operations in the time
by nonameiguess 6y ago
I don't think I can agree with that. You do a Fourier transform when the data you're working with doesn't admit easily or at all certain operations in the time domain but it does in frequency domain. If you already know that to be the case, preprocessing with a FFT is a better idea than hoping a neural network with enough layers uses a few of those to much less efficiently perform a DFT. Always take advantage of pre-existing knowledge of structure in your data. With the FFT especially, depending on how your data is being ingested, you might be able to use specialized DSPs that implement the FFT directly in hardware. These are cheap and easy to find since they're used in frequency-division multiplexing.
- dkarras 6y agoPractically, yes, absolutely! Theoretically, we want the "black box" learning system to do such feature extraction itself. At least, that is the goal.
- whimsicalism 6y ago> does not make sense to DFT data before feeding it into a multilayered neural network is false. "It would be nice if we didn't have to DFT data before feeding it into a multilayered neural network" is true, but a completely different statement.