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| Blind Source Separation× | Wiener-Filter× | |
|---|---|---|
| Fachgebiet | Signalverarbeitung | Signalverarbeitung |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 1994 | 1949 |
| Urheber≠ | Pierre Comon | Norbert Wiener |
| Typ≠ | Unsupervised signal decomposition | Linear mean-square optimal filter |
| Wegweisende Quelle≠ | Comon, P. (1994). Independent Component Analysis, a New Concept? Signal Processing, 36(3), 287–314. DOI ↗ | Wiener, N. (1949). Extrapolation, Interpolation, and Smoothing of Stationary Time Series. John Wiley & Sons. link ↗ |
| Aliasnamen≠ | BSS, Blind Signal Separation, Independent Component Analysis, ICA | Wiener Optimal Filter, Kolmogorov-Wiener Filter, Mean-Square Optimal Filter |
| Verwandt | 4 | 4 |
| Zusammenfassung≠ | Blind Source Separation (BSS) is a signal processing technique that recovers original signals from their unknown mixture without detailed knowledge of the mixing process. Through the framework of Independent Component Analysis (ICA), BSS recovers statistically independent source signals using only the assumption that sources are independent and non-Gaussian. First formalized by Pierre Comon in 1994, BSS has become essential for applications from audio separation to biomedical signal analysis. | The Wiener filter is an optimal linear filter that minimizes mean-square error between the desired signal and the filter output given knowledge of signal and noise statistics. Developed by Norbert Wiener in 1949, it provides the theoretical foundation for optimal filtering and remains the benchmark against which all other linear filtering methods are compared. |
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