Time-Varying Deep State Space Models for Sequences with Switching Dynamics
For researchers in signal processing and system identification, this work addresses the challenge of modeling time-varying systems with a novel neural network architecture.
The paper proposes time-varying state-space models with learnable dynamics via basis functions, outperforming time-invariant counterparts on synthetic switching systems and speech denoising tasks while maintaining comparable computational complexity.
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.