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Conventional Deep Learning (DL) approaches to Infinite Impulse Response (IIR) filter coefficients estimation from arbitrary frequency response are quite limited. They often suffer from inefficiencies such as tight training requirements, high complexity, and limited accuracy. As an alternative, in this paper, we explore the use of Kolmogorov-Arnold Networks (KANs) to predict the IIR filter—specifically biquad coefficients—effectively. By leveraging the high interpretability and accuracy of KANs, we achieve smooth coefficients’ optimization. Furthermore, by constraining the search space and exploring different loss functions, we demonstrate improved performance in speed and accuracy. Our approach is evaluated against other existing differentiable IIR filter solutions. The results show significant advantages of KANs over existing methods, offering steadier convergences and more accurate results. This offers new possibilities for integrating digital infinite impulse response (IIR) filters into deep-learning frameworks.

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