Abstract
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.
Chapter PDF
Similar content being viewed by others
Keywords
- Hide Markov Model
- Empirical Mode Decomposition
- Stationary Source
- Independent Component Analysis
- Source Separation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
von Bünau, P., Meinecke, F.C., Király, F.C., Müller, K.R.: Finding stationary subspaces in multivariate time series. Phys. Rev. Lett. 103, 214101 (2009)
Das, N., Routray, A., Dash, P.: A constrained least squares algorithm for fast blind source separation in a non-stationary mixing environment. In: 2011 International Conference on Energy, Automation, and Signal (ICEAS), pp. 1–6 (2011)
Hara, S., Kawahara, Y., Washio, T., von Bünau, P., Tokunaga, T., Yumoto, K.: Separation of stationary and non-stationary sources with a generalized eigenvalue problem. Neural Networks 33, 7–20 (2012)
Himberg, J., Korpiaho, K., Mannila, H., Tikanmaki, J., Toivonen, H.: Time series segmentation for context recognition in mobile devices. In: Proceedings of the IEEE International Conference on Data Mining, ICDM 2001, pp. 203–210 (2001)
Krzanowski, W.J.: Between-groups comparison of principal components. Journal of the American Statistical Association 74(367), 703–707 (1979)
Martínez-Vargas, J., Sepulveda-Cano, L., Travieso-Gonzalez, C., Castellanos-Dominguez, G.: Detection of obstructive sleep apnoea using dynamic filter-banked features. Expert Systems with Applications 39(10), 9118–9128 (2012)
Mijovic, B., De Vos, M., Gligorijevic, I., Taelman, J., Van Huffel, S.: Source separation from single-channel recordings by combining empirical-mode decomposition and independent component analysis. IEEE Trans. on Biomedical Engineering 57(9), 2188–2196 (2010)
Müller, J.S., van Bünau, P., Meinecke, F.C., Király, F.J., Müller, K.R.: The stationary subspace analysis toolbox. J. Mach. Learn. Res. 12, 3065–3069 (2011)
Rezek, I., Roberts, S.: Ensemble hidden markov models with extended observation densities for biosignal analysis. In: Husmeier, D., Dybowski, R., Roberts, S. (eds.) Probabilistic Modeling in Bioinformatics and Medical Informatics. Advanced Information and Knowledge Processing, pp. 419–450. Springer, London (2005)
Sepulveda-Cano, L.M., Acosta-Medina, C.D., Castellanos-Dominguez, G.: Finite rank series modeling for discrimination of non-stationary signals. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.) CIARP 2012. LNCS, vol. 7441, pp. 691–698. Springer, Heidelberg (2012)
Woolrich, M.W., Baker, A., Luckhoo, H., Mohseni, H., Barnes, G., Brookes, M., Rezek, I.: Dynamic state allocation for MEG source reconstruction. NeuroImage 77, 77–92 (2013)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Castro-Hoyos, C., Grisales-Franco, F.M., Martínez-Vargas, J.D., Acosta-Medina, C.D., Castellanos-Domínguez, G. (2014). Stationary Signal Separation Using Multichannel Local Segmentation. In: Bayro-Corrochano, E., Hancock, E. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2014. Lecture Notes in Computer Science, vol 8827. Springer, Cham. https://doi.org/10.1007/978-3-319-12568-8_23
Download citation
DOI: https://doi.org/10.1007/978-3-319-12568-8_23
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12567-1
Online ISBN: 978-3-319-12568-8
eBook Packages: Computer ScienceComputer Science (R0)