Variable Step Size Adaptive Blind Source Separation Algorithm – EASI

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Resource Overview

Signal separation is a common procedure in signal processing. Blind source separation refers to separating received signals without any prior knowledge of them. This implementation demonstrates one of the most important variable step size adaptive blind source separation algorithms – EASI, which includes code-level implementation details and algorithmic explanations.

Detailed Documentation

Signal separation is frequently employed in signal processing workflows. The process of separating received signals without any prior knowledge about them, known as blind source separation, represents a critically important processing methodology. Among blind source separation algorithms, the EASI algorithm stands as a variable step size adaptive approach that finds widespread application in signal processing domains. The algorithm dynamically adjusts its learning rate during iteration, typically implementing whitening preprocessing and separation matrix updates through matrix operations and nonlinear functions. Key implementation aspects include covariance matrix estimation for decorrelation and adaptive step size control based on separation performance metrics.

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