Biquad Coefficients Optimization via Kolmogorov-Arnold Networks
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.