Copyright | (c) Henning Thielemann 2008-2009 |
---|---|
License | GPL |
Maintainer | synthesizer@henning-thielemann.de |
Stability | provisional |
Portability | requires multi-parameter type classes |
Safe Haskell | None |
Language | Haskell2010 |
- centroid :: (C q, C u) => SignalRate u q amp q -> T u q
- length :: (C t, C u) => SignalRate u t amp yv -> T u t
- beginning :: (C y, C v, Transform sig y) => T rate (Dimensional v y) (sig y) -> T v y
- end :: (C y, C v, Transform sig y) => T rate (Dimensional v y) (sig y) -> T v y
- normMaximum :: (C y, C u, C v) => Signal u t v y y -> T v y
- normVectorMaximum :: (C q yv, Ord q, C u, C v) => Signal u q v q yv -> T v q
- normEuclideanSqr :: (C q, C u, C v) => Signal u q v q q -> T (Mul u (Sqr v)) q
- normVectorEuclideanSqr :: (C q yv, C q, C u, C v) => Signal u q v q yv -> T (Mul u (Sqr v)) q
- normSum :: (C q, C q, C u, C v) => Signal u q v q q -> T (Mul u v) q
- normVectorSum :: (C q yv, C q, C u, C v) => Signal u q v q yv -> T (Mul u v) q
- normMaximumProc :: (C y, C u, C v) => T s u y (R s v y y -> T v y)
- normVectorMaximumProc :: (C y yv, Ord y, C u, C v) => T s u y (R s v y yv -> T v y)
- normEuclideanSqrProc :: (C q, C u, C v) => T s u q (R s v q q -> T (Mul u (Sqr v)) q)
- normVectorEuclideanSqrProc :: (C y yv, C y, C u, C v) => T s u y (R s v y yv -> T (Mul u (Sqr v)) y)
- normSumProc :: (C q, C q, C u, C v) => T s u q (R s v q q -> T (Mul u v) q)
- normVectorSumProc :: (C y yv, C y, C u, C v) => T s u y (R s v y yv -> T (Mul u v) y)
- histogram :: (C q, C u, C v) => Signal u q v q q -> T s v q (Int, R s (DimensionGradient v u) q q)
- zeros :: (Ord q, C q, C u, C v) => T s u q (R s v q q -> R s (Recip u) q q)
Documentation
normVectorMaximum :: (C q yv, Ord q, C u, C v) => Signal u q v q yv -> T v q Source #
Manhattan norm.
normEuclideanSqr :: (C q, C u, C v) => Signal u q v q q -> T (Mul u (Sqr v)) q Source #
Square of energy norm.
Could also be called variance
.
normVectorEuclideanSqr :: (C q yv, C q, C u, C v) => Signal u q v q yv -> T (Mul u (Sqr v)) q Source #
Energy norm.
normVectorMaximumProc :: (C y yv, Ord y, C u, C v) => T s u y (R s v y yv -> T v y) Source #
Manhattan norm.
normEuclideanSqrProc :: (C q, C u, C v) => T s u q (R s v q q -> T (Mul u (Sqr v)) q) Source #
Square of energy norm.
Could also be called variance
.
normVectorEuclideanSqrProc :: (C y yv, C y, C u, C v) => T s u y (R s v y yv -> T (Mul u (Sqr v)) y) Source #
Energy norm.
normVectorSumProc :: (C y yv, C y, C u, C v) => T s u y (R s v y yv -> T (Mul u v) y) Source #
Sum norm.
histogram :: (C q, C u, C v) => Signal u q v q q -> T s v q (Int, R s (DimensionGradient v u) q q) Source #
zeros :: (Ord q, C q, C u, C v) => T s u q (R s v q q -> R s (Recip u) q q) Source #
Detects zeros (sign changes) in a signal. This can be used as a simple measure of the portion of high frequencies or noise in the signal. The result has a frequency as amplitude. If you smooth it, you will get a curve that represents a frequency progress. It ca be used as voiced/unvoiced detector in a vocoder.
The result will be one value shorter than the input.