Abstract
The purpose of this paper is to develop novel Blind Source Separation (BSS) algorithms from linear mixtures of them, which enable to separate and extract (Blind Signal Extraction (BSE)) dependent source signals. Most of the proposed algorithms for solving BSS problem rely on independence or at least uncorrelation assumption of source signals. However, in practice, the latent sources are usually dependent to some extent. On the other hand, there is a large variety of applications that require considering sources that usually behave light or strong dependence. The proposed algorithm is developed based on the wavelet coefficient representations using continuous wavelet transformation(CWT) which only requires slight differences in the CWT coefficient of the considered signals in the same scale. Moreover the proposed algorithm can extract the desired signals in the overcomplete conditions. Simulation results show that the proposed algorithm is able to extract the dependent signals and yield ideal performance.
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Li, R., Wang, F. (2007). Efficient Wavelet Based Blind Source Separation Algorithm for Dependent Sources. In: Cao, BY. (eds) Fuzzy Information and Engineering. Advances in Soft Computing, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71441-5_47
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DOI: https://doi.org/10.1007/978-3-540-71441-5_47
Publisher Name: Springer, Berlin, Heidelberg
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