| Paper: | SPTM-P7.8 | ||
| Session: | Stationary Signals and Spectrum Analysis | ||
| Time: | Thursday, May 18, 10:00 - 12:00 | ||
| Presentation: | Poster | ||
| Topic: | Signal Processing Theory and Methods: Stationary Signals and Spectrum Analysis | ||
| Title: | Efficient Kalman Smoothing for Harmonic State-Space Models | ||
| Authors: | David Barber, IDIAP Research Institute, Switzerland | ||
| Abstract: | 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. | ||
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