4 ms·

Now I wonder if a NN would be able to learn a nonlinear effect without aliasing, even if run at the original sample rate. Oversampling and filtering are, after

Now I wonder if a NN would be able to learn a nonlinear effect without aliasing, even if run at the original sample rate. Oversampling and filtering are, after all, things that could become part of the model too. Perhaps it can learn to approximate them with less CPU cost than doing it for real.

duped 5y ago
Oversampling requires producing more output information than input information. It would be incredible for a NN to realize a system that could do this without requiring more memory and CPU cycles than a good oversampling algorithm, which can be derived analytically with various definitions of "optimal." A 3rd or 4th order polynomial interpolator is pretty darn good and doesn't need a NN to find the coefficients.
- marcan_42 5y ago
  I'm not talking about using a NN for oversampling; I'm talking about whether an NN could learn to reduce aliasing when implementing nonlinear functions without any external oversampling. Oversampled DSP algorithms work by oversampling, performing the nonlinear processing, then filtering to remove the higher harmonics, and finally downsampling again. We do it this way because it's convenient and easy to understand and based on proven mathematics. But nothing says these steps have to be distinct. An oversampled DSP algorithm looks like a regular DSP algorithm from the outside, perhaps with some more state and latency required for it to perform the internal oversampling. You can also imolement such an oversampled algorithm entirely at the original sample rate clock; it just means the processing needs to internally process several samples per outer loop sample. Since neural networks excel at modeling "black boxes" as one amorphous blob that we don't understand, I wonder if a NN could learn to model such an internally oversampled algorithm fairly accurately, and what the computational complexity would be. Since you can model the oversampling/filtering/etc steps as linear convolutions with wider internal state at the original sample rate, I'm almost certain this will work with the right NN topology. It's obvious an NN can implement oversampling. And so my question is: could treating the combined oversampled processing as one step, and training a NN on that, potentially result in a more efficient implementation than doing it naively? Especially for heavy distortion that needs high oversampling ratios.

duped 5y ago

Oversampling requires producing more output information than input information. It would be incredible for a NN to realize a system that could do this without requiring more memory and CPU cycles than a good oversampling algorithm, which can be derived analytically with various definitions of "optimal." A 3rd or 4th order polynomial interpolator is pretty darn good and doesn't need a NN to find the coefficients.

marcan_42 5y ago
I'm not talking about using a NN for oversampling; I'm talking about whether an NN could learn to reduce aliasing when implementing nonlinear functions without any external oversampling. Oversampled DSP algorithms work by oversampling, performing the nonlinear processing, then filtering to remove the higher harmonics, and finally downsampling again. We do it this way because it's convenient and easy to understand and based on proven mathematics. But nothing says these steps have to be distinct. An oversampled DSP algorithm looks like a regular DSP algorithm from the outside, perhaps with some more state and latency required for it to perform the internal oversampling. You can also imolement such an oversampled algorithm entirely at the original sample rate clock; it just means the processing needs to internally process several samples per outer loop sample. Since neural networks excel at modeling "black boxes" as one amorphous blob that we don't understand, I wonder if a NN could learn to model such an internally oversampled algorithm fairly accurately, and what the computational complexity would be. Since you can model the oversampling/filtering/etc steps as linear convolutions with wider internal state at the original sample rate, I'm almost certain this will work with the right NN topology. It's obvious an NN can implement oversampling. And so my question is: could treating the combined oversampled processing as one step, and training a NN on that, potentially result in a more efficient implementation than doing it naively? Especially for heavy distortion that needs high oversampling ratios.