Adaptive Signal Separation with Maximum Likelihood


Yongjian Zhao, Bin Jiang, Journal of Information Processing Systems Vol. 16, No. 1, pp. 145-154, Feb. 2020  

https://doi.org/10.3745/JIPS.04.0162
Keywords: Density, Estimator, Framework, Kurtosis, Likelihood, Separation
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Abstract

Maximum likelihood (ML) is the best estimator asymptotically as the number of training samples approaches infinity. This paper deduces an adaptive algorithm for blind signal processing problem based on gradient optimization criterion. A parametric density model is introduced through a parameterized generalized distribution family in ML framework. After specifying a limited number of parameters, the density of specific original signal can be approximated automatically by the constructed density function. Consequently, signal separation can be conducted without any prior information about the probability density of the desired original signal. Simulations on classical biomedical signals confirm the performance of the deduced technique.


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Cite this article
[APA Style]
Zhao, Y. & Jiang, B. (2020). Adaptive Signal Separation with Maximum Likelihood. Journal of Information Processing Systems, 16(1), 145-154. DOI: 10.3745/JIPS.04.0162.

[IEEE Style]
Y. Zhao and B. Jiang, "Adaptive Signal Separation with Maximum Likelihood," Journal of Information Processing Systems, vol. 16, no. 1, pp. 145-154, 2020. DOI: 10.3745/JIPS.04.0162.

[ACM Style]
Yongjian Zhao and Bin Jiang. 2020. Adaptive Signal Separation with Maximum Likelihood. Journal of Information Processing Systems, 16, 1, (2020), 145-154. DOI: 10.3745/JIPS.04.0162.