A Hypernetwork-Based Approach to KAN Representation of Audio Signals

• URL: http://arxiv.org/abs/2503.02585v1
• Date: Tue, 04 Mar 2025 13:08:45 GMT
• Title: A Hypernetwork-Based Approach to KAN Representation of Audio Signals
• Authors: Patryk Marszałek, Maciej Rut, Piotr Kawa, Piotr Syga,
• Abstract summary: Implicit neural representations (INR) have gained prominence for efficiently encoding multimedia data, yet their applications in audio signals remain limited.<n>This study introduces the Kolmogorov-Arnold Network (KAN), a novel architecture using learnable activation functions, as an effective INR model for audio representation.<n>To extend KAN's utility, we propose FewSound, a hypernetwork-based architecture that enhances INR parameter updates.
• Score: 1.7499351967216343
• License: http://creativecommons.org/licenses/by/4.0/
• 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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