Abstract
Nonlinear source separation can be performed by inferring the state of a nonlinear state-space model. We study and improve the inference algorithm in the variational Bayesian blind source separation model introduced by Valpola and Karhunen in 2002. As comparison methods we use extensions of the Kalman filter that are widely used inference methods in tracking and control theory. The results in stability, speed, and accuracy favour our method especially in difficult inference problems.
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Keywords
- Independent Component Analysis
- Source Separation
- Blind Source Separation
- Inference Algorithm
- Speech Spectrum
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
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Raiko, T., Tornio, M., Honkela, A., Karhunen, J. (2006). State Inference in Variational Bayesian Nonlinear State-Space Models. In: Rosca, J., Erdogmus, D., Príncipe, J.C., Haykin, S. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2006. Lecture Notes in Computer Science, vol 3889. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11679363_28
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DOI: https://doi.org/10.1007/11679363_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32630-4
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