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Thanks for the reply! This work is fascinating and while I'm not a python guy I'm going to play with your library a bunch.

I do think you should investigate comparisons to adaptive FIRs much more. This field is critical to the design of low power medical devices like hearing aids, which need feedback reduction, echo cancellation, and the like with minimal filter orders.

My question on correlated parameters was a bit more abstract. Often in the design of classical audio signal processors for creative applications you find that the user space parameters can be correlated, which map to more design space parameters that are even more correlated, and down to implementation level parameters which are even more correlated. For example in a filter designed by frequency sampling, the adjacent bins of an FFT are highly correlated in their I/O and I was curious if you optimized a bit by taking a DCT or similar approach for reparameterization like you'd find in calculating MFCCs and the like. It's really tough to design ML approaches for creative signal processing that are better than traditional methods due to this nature, humans learn and adapt to correlations very quickly, machines not so much when dealing with oscillation and ripple. Many local extrema in the parameter space and all that.

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Adaptive IIR would be more interesting, as automatically controlling and designing those filters in a stable way is rather hard. And they're both differentiable and power efficient. Especially anything that is not a biquad series, and because they have recursion related computational noise, which the ANN should be able to optimize out.