Abstract
“Blind Source Separation (BSS)” (i.e, Estimating independent source signals based on observed signals) problems can be found in many areas, such as communication, electrical engineering, and biomedical engineering. A famous example of BSS is the so-called “cocktail party problem”; in a cocktail party, we want to separate the source signals (e.g., speech and other noises) based on the observed signals collected by two microphones. Independent Component Analysis (ICA) is the most commonly used approach for the BSS problem. The objective function used in the traditional ICA algorithm is measured by non-gaussianity of observed signals. We propose a new ICA algorithm, named the MIK-ICA. MIK captures 3 key words: Mean-Inequality and Kernel-Density-Estimation. The objective function used in the proposed MIK-ICA is statistical independence, in which we combine mean-inequality and kernel density estimation techniques. Experimental results show that the proposed MIK-ICA works as well as many traditional ICA algorithms to separate studied simulated signals from noises and to separate studied pulse signals from noises.