Efficient Kalman Smoothing for Harmonic State-Space Models

Harmonic probabilistic models are common in signal analysis. Framed as a linear-Gaussian state-space model, smoothed inference scales as $O(TH^2)$ where $H$ is twice the number of frequencies in the model and $T$ is the length of the time-series. Due to their central role in acoustic modelling, fast effective inference in this model is of some considerable interest. We present a form of `rotation-corrected' low-rank approximation for the backward pass of the Rauch-Tung-Striebel smoother. This provides an effective approximation with computation complexity $O(TSH)$ where $S$ is the rank of the approximation.


Year:
2005
Publisher:
IDIAP
Keywords:
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 Record created 2006-03-10, last modified 2018-01-27

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