Natural Gradient Blind Source Separation Algorithm

The program referenced in this document performs natural gradient blind source separation (BSS) using nonlinear functions. Natural gradient BSS is a signal processing technique designed to recover original source signals from mixed observations, with wide-ranging applications in speech recognition, image processing, biomedical signal analysis, and other domains. The algorithm implementation typically involves optimizing separation matrices through natural gradient descent, where nonlinear functions (often tanh or sigmoid activations) help model the probability distributions of source signals. These nonlinear components are crucial for handling non-Gaussian source characteristics and improving separation accuracy. Through iterative processing of mixed signals using gradient-based optimization, the algorithm generates approximations of the original source signals, thereby achieving effective signal separation. The implementation commonly includes steps for signal whitening, weight matrix initialization, and adaptive learning rate selection to ensure convergence. Natural gradient BSS remains a fundamental signal processing technique with significant practical importance across numerous applications.