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arXiv · 2503.02585

As Good as It KAN Get: High-Fidelity Audio Representation

Patryk Marszałek·Maciej Rut·Piotr Kawa·Przemysław Spurek·Piotr Syga
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Abstract

Implicit neural representations (INR) have gained prominence for efficiently encoding multimedia data, yet their applications in audio signals remain limited. This study introduces the Kolmogorov-Arnold Network (KAN), a novel architecture using learnable activation functions, as an effective INR model for audio representation. KAN demonstrates superior perceptual performance over previous INRs, achieving the lowest Log-SpectralDistance of 1.29 and the highest Perceptual Evaluation of Speech Quality of 3.57 for 1.5 s audio. To extend KAN's utility, we propose FewSound, a hypernetwork-based architecture that enhances INR parameter updates. FewSound outperforms the state-of-the-art HyperSound, with a 33.3% improvement in MSE and 60.87% in SI-SNR. These results show KAN as a robust and adaptable audio representation with the potential for scalability and integration into various hypernetwork frameworks. The source code can be accessed at https://github.com/gmum/fewsound.git.

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Patryk Marszałek, Maciej Rut, Piotr Kawa, Przemysław Spurek, Piotr Syga. 2025-03-04. As Good as It KAN Get: High-Fidelity Audio Representation. https://doi.org/10.1145/3746252.3761405

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