Hacker News Reader

Comment by hcrisp | Hacker News Reader

If the FIR filters are time variant, what determines the values of the kernel at any given moment - is it the prior values of the signal being generated or something else?

For IIR filters, I imagine the network would have to be recurrent. If you train the weights with labeled data, does that amount to a different mechanism for designing a filter, sort of like exchanging bilinear transformation for a data-driven approach? The former seems like forward engineering from first principles, whereas the latter seems like reverse engineering from data that exhibits transformations that you want the filter to encode. Fascinating stuff.

View on HN

I imagine the authors keep filter order as a design parameter, since dynamically changing order is a rather tricky proposition.

For IIRs the approach (imo) would be to use transformed analog biquads (such as an SVF topology, via the TPT/Zavalishin's method) which is designed to handle the time variant issues with state evolution. From there your model wouldn't synthesize filter coefficients directly but either the design parameters of the filter or the gains of the outputs or multi mode filters built from cascaded biquad sections. A good example of this in practice is the Oberheim "pole mixing" filter (analog) or the Shruthi's variation on it, which is a 4th order filter that achieves a wide array of frequency responses by mixing the outputs of each stage. Those gains in the mixer can be varied for very cool results.

Hi, yup that's true we keep the filter order fixed. For the experiments in the paper, the time-varying coefficients are generated by a neural network that is trained end-2-end to generate audio like the training set (conditioned on high-level controls such pitch and loudness).

I agree that IIRs are a great avenue of future study, also with time-varying coefficients. I've played around a bit with them, but they are harder to efficiently train with current autodiff software and GPUs/TPUs. I think they may require writing a custom cuda kernel, but I'm hopeful for things like JAX's scan operation.

Reply on news.ycombinator.com