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Wiener filter
Original title: Wiener filter
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The goal of the Wiener filter is to compute a statistical estimate of an unknown signal using a related signal as an input and filtering it to produce the estimate. For example, the known signal might consist of an unknown signal of interest that has been corrupted by additive noise. The Wiener filter can be used to filter out the noise from the corrupted signal to provide an estimate of the underlying signal of interest. The Wiener filter is based on a statistical approach, and a more statistical account of the theory is given in the minimum mean square error (MMSE) estimator article.
https://scrapbox.io/files/69104f38d9f7ff67ac125d76.png
https://scrapbox.io/files/69104f4c96c14a4ca10a99e8.png
The goal of the Wiener filter is to compute a statistical estimate of an unknown signal using a related signal as an input and filtering it to produce the estimate. For example, the known signal might consist of an unknown signal of interest that has been corrupted by additive noise. The Wiener filter can be used to filter out the noise from the corrupted signal to provide an estimate of the underlying signal of interest. The Wiener filter is based on a statistical approach, and a more statistical account of the theory is given in the minimum mean square error (MMSE) estimator article.
Source updated 2025-11-09 · Snapshot 2026-10-08
Source links and calculated neighbors
Cosine values measure shared lexical features, not truth, agreement or identical meaning. Original reference links are labeled separately.
- comb filterComputed lexical cosine similarity 0.131 · shared title, text, tags and references
- inverse comb filterComputed lexical cosine similarity 0.101 · shared title, text, tags and references
- DDSP: Differentiable Digital Signal ProcessingComputed lexical cosine similarity 0.087 · shared title, text, tags and references