Abstract
We present independent slow feature analysis as a new method for nonlinear blind source separation. It circumvents the indeterminacy of nonlinear independent component analysis by combining the objectives of statistical independence and temporal slowness. The principle of temporal slowness is adopted from slow feature analysis, an unsupervised method to extract slowly varying features from a given observed vectorial signal. The performance of the algorithm is demonstrated on nonlinearly mixed speech data.
Keywords
- Signal Component
- Independent Component Analysis
- Blind Source Separation
- Temporal Slowness
- Slow Feature Analysis
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.
This work has been supported by the Volkswagen Foundation through a grant to LW for a junior research group.
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Blaschke, T., Wiskott, L. (2004). Independent Slow Feature Analysis and Nonlinear Blind Source Separation. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_94
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DOI: https://doi.org/10.1007/978-3-540-30110-3_94
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