Abstract
Blind source separation algorithms are based on various separation criteria. Differences in convolution kernels of the sources are common assumptions in audio and image processing. Since it is still an ill posed problem, any additional information is beneficial. In this contribution, we investigate the use of sparsity criteria for both the source signal and the convolution kernels. A probabilistic model of the problem is introduced and its Variational Bayesian solution derived. The sparsity of the solution is achieved by introduction of unknown variance of the prior on all elements of the convolution kernels and the mixing matrix. Properties of the model are analyzed on simulated data and compared with state of the art methods. Performance of the algorithm is demonstrated on the problem of decomposition of a sequence of medical data. Specifically, the assumption of sparseness is shown to suppress artifacts of unconstrained separation method.
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Keywords
- Blind Source Separation
- Convolution Kernel
- Blind Deconvolution
- Renal Scintigraphy
- Blind Source Separation Algorithm
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.
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Šmídl, V., Tichý, O. (2013). Sparsity in Bayesian Blind Source Separation and Deconvolution. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2013. Lecture Notes in Computer Science(), vol 8189. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40991-2_35
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DOI: https://doi.org/10.1007/978-3-642-40991-2_35
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