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Everything about Signal separation

Separation of a set of source signals from a set of mixed signals

Source separation, blind signal separation (BSS) or blind source separation, is the separation of a set of source signals from a set of mixed signals, without the aid of information about the source signals or the mixing process. It is most commonly applied in digital signal processing and involves the analysis of mixtures of signals; the objective is to recover the original component signals from a mixture signal. The classical example of a source separation problem is the cocktail party problem, where a number of people are talking simultaneously in a room, and a listener is trying to follow one of the discussions. The human brain can handle this sort of auditory source separation problem, but it is a difficult problem in digital signal processing. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Signal separationA 5-minute orientation
  2. Skim a recent paper: Signal-induced NLRP3 phase separation initiates inflammasome activationInfluential work since 2022
  3. Pick up a classic book: Blind Source SeparationThe most-reprinted book on the subject
  4. Read the landmark paper: Blind signal separation: statistical principlesCited 1,380 times
  5. Go sideways: Independent component analysisA closely related topic
  6. Go sideways: Computational auditory scene analysisA closely related topic

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