soundgen (version 3.0.0)

gaussianSmooth2D: Gaussian smoothing in 2D

Description

Takes a matrix of numeric values and smooths it by convolution with a symmetric Gaussian window function. Values outside the matrix are either treated as zero, attenuating the edges, or assumed to continue beyond the edges.

Usage

gaussianSmooth2D(
  m,
  kernelSize = 5,
  kernelSD = 0.5,
  action = c("blur", "unblur"),
  amount = 0.5,
  padWith = c("repeat", "zero"),
  plotKernel = FALSE
)

Value

A numeric matrix of the same dimensions as input.

Arguments

m

input matrix (numeric, on any scale, doesn't have to be square)

kernelSize

vector of size 1 or 2: the size of the Gaussian kernel, in points (forced to odd values). kernelSize = 0 means no smoothing along that dimension.

kernelSD

the SD of the Gaussian kernel evaluated over [-1, 1]: for ex., if kernelSD = 0.5, the kernel spans approximately ±2 SDs

action

'blur' = kernel-weighted average, 'unblur' = unsharp masking

amount

the amount of residual to mix with the original when unblurring: \(result = orig + amount * (orig - blurred)\)

padWith

how to treat the edges of the matrix: 'repeat' = the edge rows/columns are assumed to continue beyond the matrix; 'zero' = values outside the matrix are assumed to be zero, attenuating the smoothed edges

plotKernel

if TRUE, plots the kernel

See Also

modulationSpectrum spectrogram

Examples

data('speechEx', package = 'soundgen')
s = spectrogram(speechEx, from = 0, to = 1, windowLength = 10,
  output = 'original', plot = FALSE)
s = log(s + .001)
image(t(s))
s1 = gaussianSmooth2D(s, kernelSize = 5, plotKernel = TRUE)
image(t(s1))

# more smoothing in time than in frequency
s2 = gaussianSmooth2D(s, kernelSize = c(5, 15))
image(t(s2))

# vice versa - more smoothing in frequency
s3 = gaussianSmooth2D(s, kernelSize = c(25, 3))
image(t(s3))

# smoothing only in one dimension
s4 = gaussianSmooth2D(s, kernelSize = c(25, 0))
image(t(s4))
s5 = gaussianSmooth2D(s, kernelSize = c(0, 15))
image(t(s5))

# sharpen the image
s6 = gaussianSmooth2D(s, kernelSize = 5, action = 'unblur', amount = .5)
image(t(s6))