Time-Varying Deep State Space Models for Sequences with Switching Dynamics
摘要
The identification and modeling of time-varying systems is a fundamental challenge in signal processing and system identification. To address this challenge, we propose a class of time-varying state-space model (SSM) based neural networks in which the neurons' states are governed by time-varying dynamics. The proposed model provides the learnable time-varying dynamics through a dictionary of basis functions, where each basis function evolves differently over time. We evaluate the proposed approach on both synthetic data from switching systems and a speech denoising task where real audio is corrupted with switching dynamics noise. The results show that the proposed time-varying model consistently outperforms its time-invariant counterparts while maintaining comparable computational complexity. Our investigations also reveal which aspects of the time-varying dynamics of the data most need to be captured by the proposed time-invariant models, how the additional freedom provided by time-varying basis functions should be allocated across model components, and to what extent larger models can compensate for time-invariant limitations.
引用
@article{arxiv.2605.15311,
title = {Time-Varying Deep State Space Models for Sequences with Switching Dynamics},
author = {Sanja Karilanova and Subhrakanti Dey and Ayça Özçelikkale},
journal= {arXiv preprint arXiv:2605.15311},
year = {2026}
}