Statistical Component Separation for Targeted Signal Recovery in Noisy Mixtures

Understand

Separating signals from an additive mixture may be an unnecessarily hard problem when one is only interested in specific properties of a given signal.

• In this work, we tackle simpler "statistical component separation" problems that focus on recovering a predefined set of statistical descriptors of a target signal from a noisy mixture.
• Assuming access to samples of the noise process, we investigate a method devised to match the statistics of the solution candidate corrupted by noise samples with those of the observed mixture.
• We first analyze the behavior of this method using simple examples with analytically tractable calculations.