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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Stationary Signal Separation Using Multichannel Local Segmentation
verfasst von : C. Castro-Hoyos, F. M. Grisales-Franco, J. D. Martínez-Vargas, Carlos D. Acosta-Medina, Germán Castellanos-Domínguez
Erschienen in: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Verlag: Springer International Publishing
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In this work, we study the influence of locally stationary segments as preprocess stage to separate stationary and non-stationary segments. To this, we compare three different segmentation approaches, namely i)cumulative variance based segmentation, ii)PCA based segmentation, and iii)HMM based segmentation. Results are measured as the true and false detection probabilities, and also as the ratio between the real and estimated number of segments. Finally, to achieve the separation, we use the Analytic Stationary Subspace Analysis (ASSA) and results are measured as the correlation between the true and the estimated stationary sources. In this case, we also compare against the best possible ASSA solution. Results show that inclusion of locally stationary segments could enhance or at least achieve optimal estimation of stationary sources.