Probabilistic blind deconvolution of non-stationary sources |
Rasmus Kongsgaard Olsson, Lars Kai Hansen
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| Abstract | We solve a class of blind signal separation problems using a constrained linear Gaussian model. The observed signal is modelled by a convolutive mixture of colored noise signals with additive white noise. We derive a time-domain EM algorithm `KaBSS' which estimates the source signals, the associated second-order statistics, the mixing filters and the observation noise
covariance matrix. KaBSS invokes the Kalman smoother in the E-step to infer the posterior probability of the sources, and one-step lower bound optimization of the mixing filters and noise covariance in the M-step. In line with (Parra and Spence, 2000) the source signals are assumed time variant in order to constrain the solution sufficiently. Experimental results are shown for mixtures of speech signals. |
| Keywords | Blind source separation, independent component analysis, speech processing |
| Type | Conference paper [With referee] |
| Conference | 12th European Signal Processing Conference |
| Year | 2004 Month September pp. 1697-1700 |
| Electronic version(s) | [pdf] |
| BibTeX data | [bibtex] |
| IMM Group(s) | Intelligent Signal Processing |