Dat, P. T., & Dat, T. H. (2025). KAN-AST: Kolmogorov-Arnold Network based Audio Spectrogram Transformer for Audio Classification. In (Editor), 2025 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU). https://doi.org/10.1109/asru65441.2025.11434708
Abstract:
Recently, Kolmogorov-Arnold Networks (KANs) have emerged as a promising alternative to enhance the performance of Multi-Layer Perceptrons (MLPs). Instead of relying on traditional linear weights, KANs utilize spline-parametrized univariate functions, significantly improving interpretability and enabling dynamic learning of activation patterns. This novel approach has garnered considerable interest from the AI community and has seen rapid global adoption. While research on applying KANs to Machine Learning and Computer Vision has expanded, their application in audio processing remains largely unexplored. In this paper, we investigate the untapped potential of KANs in improving models for audio tasks. We propose the first Kolmogorov-Arnold Network-based Audio Spectrogram Transformer (KAN-AST) for audio classification. Our study demonstrates how replacing MLPs with KANs enhances the performance of the AST model, highlighting the potential of KANs in advancing audio classification.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Industry funded - EC-2023-105
Grant Reference no. : NA