From KAN to GR-KAN: Advancing Speech Enhancement with KAN-Based Methodology

Haoyang Li; Yuchen Hu; Chen Chen; Sabato Marco Siniscalchi; Songting Liu; Eng Siong Chng

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arXiv · 2412.17778

From KAN to GR-KAN: Advancing Speech Enhancement with KAN-Based Methodology

Haoyang Li·Yuchen Hu·Chen Chen·Sabato Marco Siniscalchi·Songting Liu·Eng Siong Chng

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Abstract

Deep neural network (DNN)-based speech enhancement (SE) usually uses conventional activation functions, which lack the expressiveness to capture complex multiscale structures needed for high-fidelity SE. Group-Rational KAN (GR-KAN), a variant of Kolmogorov-Arnold Networks (KAN), retains KAN's expressiveness while improving scalability on complex tasks. We adapt GR-KAN to existing DNN-based SE by replacing dense layers with GR-KAN layers in the time-frequency (T-F) domain MP-SENet and adapting GR-KAN's activations into the 1D CNN layers in the time-domain Demucs. Results on Voicebank-DEMAND show that GR-KAN requires up to 4x fewer parameters while improving PESQ by up to 0.1. In contrast, KAN, facing scalability issues, outperforms MLP on a small-scale signal modeling task but fails to improve MP-SENet. We demonstrate the first successful use of KAN-based methods for consistent improvement in both time- and SoTA TF-domain SE, establishing GR-KAN as a promising alternative for SE.

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BibTeX RIS

Haoyang Li, Yuchen Hu, Chen Chen, Sabato Marco Siniscalchi, Songting Liu, Eng Siong Chng. 2024-12-23. From KAN to GR-KAN: Advancing Speech Enhancement with KAN-Based Methodology. https://arxiv.org/abs/2412.17778

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