gaussianSmooth2D: Gaussian smoothing in 2D

In soundgen: Sound Synthesis and Acoustic Analysis

Gaussian smoothing in 2D

Description

Takes a matrix of numeric values and smoothes it by convolution with a symmetric Gaussian window function.

Usage

gaussianSmooth2D(
  m,
  kernelSize = 5,
  kernelSD = 0.5,
  action = c("blur", "unblur")[1],
  plotKernel = FALSE
)

Arguments

m	input matrix (numeric, on any scale, doesn't have to be square)
kernelSize	the size of the Gaussian kernel, in points
kernelSD	the SD of the Gaussian kernel relative to its size (.5 = the edge is two SD's away)
action	'blur' = kernel-weighted average, 'unblur' = subtract kernel-weighted average
plotKernel	if TRUE, plots the kernel

Value

Returns a numeric matrix of the same dimensions as input.

See Also

modulationSpectrum

Examples

s = spectrogram(soundgen(), samplingRate = 16000, windowLength = 10,
  output = 'original', plot = FALSE)
s = log(s + .001)
# image(s)
s1 = gaussianSmooth2D(s, kernelSize = 5, plotKernel = TRUE)
# image(s1)

## Not run: 
# more smoothing in time than in frequency
s2 = gaussianSmooth2D(s, kernelSize = c(5, 15))
image(s2)

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

# sharpen the image by deconvolution with the kernel
s4 = gaussianSmooth2D(s1, kernelSize = 5, action = 'unblur')
image(s4)

s5 = gaussianSmooth2D(s, kernelSize = c(15, 1), action = 'unblur')
image(s5)

## End(Not run)

soundgen documentation built on Sept. 29, 2023, 5:09 p.m.