用可变动态的神经状态空间模型，更好捕捉随时间变化的系统行为。
Time-Varying Deep State Space Models for Sequences with Switching Dynamics
- 通过基函数字典实现可学习的时变动态，各基函数独立演化。
- 在切换系统与语音去噪任务中均优于固定动态模型。
- 揭示了时变特性对建模的关键影响，适合动态系统研究者。
时变系统的识别与建模是信号处理与系统辨识中的基本挑战。为此，我们提出一类基于神经网络的时变状态空间模型（SSM），其中神经元状态由时变动态驱动。该模型通过一组基函数字典实现可学习的时变动态，每个基函数随时间以不同方式演化。我们在来自切换系统的合成数据以及真实语音受切换噪声污染的去噪任务上评估了该方法。结果表明，所提时变模型始终优于其时不变对手，同时保持相当的计算复杂度。我们的研究还揭示了时变动态中哪些方面最需被模型捕捉，时变基函数应如何分配至模型各组件，以及更大模型在多大程度上能弥补时不变模型的局限性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。