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Do you think DDSP could be used for feature engineering/discovery? My setting is a time series that I want to do regression on. But it's not clear a priory what features of the series have good predictive power. I imagine making the DDSP the first layer of a FNN and the gradients helping me identify the right filters to use to extract important features from my data.

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DDSP modules are helpful in situations where you want to impose some level of interpretability and modularity. Most also don't have parameters themselves, but must have them provided by another network or variable. So you could imagine for instance feeding your data through a NN that then predicts filter coefficients, then running the same data through a filter with those coefficients (if you wanted to enforce time-varying linearity for interpretability let's say).

I don't know much about DSP, neural nets, and audio, but I am really intrigued by this project. If you have a second, could you give a simple example of how this approach could be applied to problems outside of audio?

So I'm using the gradients "grad" of the second network (the DDSP) to make a loss function for the first network, such as |grad|^2?

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