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Designing infinite impulse response filters to match an arbitrary magnituderesponse requires specialized techniques. Methods like modified Yule-Walker arerelatively efficient, but may not be sufficiently accurate in matching highorder responses. On the other hand, iterative optimization techniques oftenenable superior performance, but come at the cost of longer run-times and aresensitive to initial conditions, requiring manual tuning. In this work, weaddress some of these limitations by learning a direct mapping from the targetmagnitude response to the filter coefficient space with a neural networktrained on millions of random filters. We demonstrate our approach enables bothfast and accurate estimation of filter coefficients given a desired response.We investigate training with different families of random filters, and findtraining with a variety of filter families enables better generalization whenestimating real-world filters, using head-related transfer functions and guitarcabinets as case studies. We compare our method against existing methodsincluding modified Yule-Walker and gradient descent and show our approach is,on average, both faster and more accurate.

Direct design of biquad filter cascades with deep learning by sampling random polynomials