Time-Varying Deep State Space Models for Sequences with Switching Dynamics

• URL: http://arxiv.org/abs/2605.15311v1
• Date: Thu, 14 May 2026 18:25:04 GMT
• Title: Time-Varying Deep State Space Models for Sequences with Switching Dynamics
• Abstract summary: We propose a class of time-varying state-space model based neural networks.<n>The proposed model provides the learnable time-varying dynamics through a dictionary of basis functions.<n>We evaluate the proposed approach on both synthetic data from switching systems and a speech denoising task.
• Score: 2.309307613420651
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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