View source: R/smoothContours.R
getSmoothContour  R Documentation 
Returns a smooth contour based on an arbitrary number of anchors. Used by
soundgen
for generating intonation contour, mouth opening, etc.
This function is mostly intended to be used internally by soundgen, more
precisely to construct (upsample) smooth curves from a number of anchors. For
general upsampling or downsampling of audio, use resample
. Note
that pitch contours are treated as a special case: values are logtransformed
prior to smoothing, so that with 2 anchors we get a linear transition on a
log scale (as if we were operating with musical notes rather than frequencies
in Hz). Pitch plots have two Y axes: one showing Hz and the other showing
musical notation.
getSmoothContour( anchors = data.frame(time = c(0, 1), value = c(0, 1)), len = NULL, thisIsPitch = FALSE, normalizeTime = TRUE, interpol = c("approx", "spline", "loess")[3], loessSpan = NULL, discontThres = 0.05, jumpThres = 0.01, valueFloor = NULL, valueCeiling = NULL, plot = FALSE, xlim = NULL, ylim = NULL, samplingRate = 16000, voiced = NULL, contourLabel = NULL, NA_to_zero = TRUE, ... )
anchors 
a numeric vector of values or a list/dataframe with one column
(value) or two columns (time and value). 
len 
the required length of the output contour. If NULL, it will be
calculated based on the maximum time value (in ms) and 
thisIsPitch 
(boolean) is this a pitch contour? If true, logtransforms before smoothing and plots in both Hz and musical notation 
normalizeTime 
if TRUE, normalizes anchors$time values to range from 0 to 1 
interpol 
method of interpolation between anchors: "approx" = linear
with 
loessSpan 
controls the amount of smoothing when interpolating between
anchors with 
discontThres 
if two anchors are closer in time than

jumpThres 
if anchors are closer than 
valueFloor, valueCeiling 
lowser/upper bounds for the contour 
plot 
(boolean) produce a plot? 
xlim, ylim 
plotting options 
samplingRate 
sampling rate used to convert time values to points (Hz) 
voiced, contourLabel 
graphical pars for plotting breathing contours (see examples below) 
NA_to_zero 
if TRUE, all NAs are replaced with zero 
... 
other plotting options passed to 
Returns a numeric vector of length len
.
# long format: anchors are a dataframe a = getSmoothContour(anchors = data.frame( time = c(50, 137, 300), value = c(0.03, 0.78, 0.5)), normalizeTime = FALSE, voiced = 200, valueFloor = 0, plot = TRUE, main = '', samplingRate = 16000) # breathing # short format: anchors are a vector (equal time steps assumed) a = getSmoothContour(anchors = c(350, 800, 600), len = 5500, thisIsPitch = TRUE, plot = TRUE, samplingRate = 3500) # pitch # a single anchor gives constant value a = getSmoothContour(anchors = 800, len = 500, thisIsPitch = TRUE, plot = TRUE, samplingRate = 500) # two pitch anchors give loglinear F0 change a = getSmoothContour(anchors = c(220, 440), len = 500, thisIsPitch = TRUE, plot = TRUE, samplingRate = 500) ## Two closely spaced anchors produce a pitch jump # one loess for the entire contour a1 = getSmoothContour(anchors = list(time = c(0, .15, .2, .7, 1), value = c(360, 116, 550, 700, 610)), len = 500, thisIsPitch = TRUE, plot = TRUE, samplingRate = 500) # two segments with a linear transition a2 = getSmoothContour(anchors = list(time = c(0, .15, .17, .7, 1), value = c(360, 116, 550, 700, 610)), len = 500, thisIsPitch = TRUE, plot = TRUE, samplingRate = 500) # two segments with an abrupt jump a3 = getSmoothContour(anchors = list(time = c(0, .15, .155, .7, 1), value = c(360, 116, 550, 700, 610)), len = 500, thisIsPitch = TRUE, plot = TRUE, samplingRate = 500) # compare: plot(a2) plot(a3) # NB: the segment before the jump is upsampled to compensate ## Control the amount of smoothing getSmoothContour(c(1, 3, 9, 10, 9, 9, 2), len = 100, plot = TRUE, loessSpan = NULL) # default amount of smoothing (depends on dur) getSmoothContour(c(1, 3, 9, 10, 9, 9, 2), len = 100, plot = TRUE, loessSpan = .85) # more smoothing than default getSmoothContour(c(1, 3, 9, 10, 9, 9, 2), len = 100, plot = TRUE, loessSpan = .5) # less smoothing getSmoothContour(c(1, 3, 9, 10, 9, 9, 2), len = 100, plot = TRUE, interpol = 'approx') # linear interpolation (no smoothing) ## Upsample preserving leading and trailing NAs anchors = data.frame(time = c(1, 4, 5, 7, 10, 20, 23, 25, 30), value = c(NA, NA, 10, 15, 12, NA, 17, 15, NA)) plot(anchors, type = 'b') anchors_ups = getSmoothContour( anchors, len = 200, interpol = 'approx', # only approx can propagate NAs NA_to_zero = FALSE, # preserve NAs discontThres = 0) # don't break into subcontours plot(anchors_ups, type = 'b')
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