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- W2017519377 abstract "In this paper, we describe on-line Bayesian filtering methods for time series models with heavy-tailed /spl alpha/-stable noise. The filtering methodology is based on a scale mixtures of normals (SMiN) representation of the /spl alpha/-stable distribution, which reexpresses the intractable stable distribution in a conditionally Gaussian form. We describe how the method can be used for estimation of time-varying autoregressive signals buried in symmetric /spl alpha/-stable noise, efficiently implemented using an adaptation to an existing Rao-Blackwellized particle filter. The methodology is shown to work well with both simulated and real corrupted audio data, for which the /spl alpha/-stable noise distribution is found to fit the noise data better than other more standard heavy-tailed distributions." @default.
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- W2017519377 date "2006-02-01" @default.
- W2017519377 modified "2023-09-26" @default.
- W2017519377 title "On-line Bayesian estimation of signals in symmetric /spl alpha/-stable noise" @default.
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- W2017519377 doi "https://doi.org/10.1109/tsp.2005.861886" @default.
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