Nothing
R/amplitude.R
In soundgen: Sound Synthesis and Acoustic Analysis
Defines functions
killDC
getEnv
.addAM
addAM
transplantEnv
.flatEnv
flatEnv
normalizeFolder
.getRMS
getRMS
Documented in
addAM
.addAM
flatEnv
.flatEnv
getEnv
getRMS
.getRMS
killDC
normalizeFolder
transplantEnv
#' RMS amplitude
#'
#' Calculates root mean square (RMS) amplitude in overlapping windows, providing
#' an envelope of RMS amplitude - a measure of sound intensity. Longer windows
#' provide smoother, more robust estimates; shorter windows and more overlap
#' improve temporal resolution, but they also increase processing time and make
#' the contour less smooth.
#'
#' Note that you can also get similar estimates per frame from
#' \code{\link{analyze}} on a normalized scale of 0 to 1, but \code{getRMS} is
#' much faster, operates on the original scale, and plots the amplitude contour.
#' If you need RMS for the entire sound instead of per frame, you can simply
#' calculate it as \code{sqrt(mean(x^2))}, where \code{x} is your waveform.
#' Having RMS estimates per frame gives more flexibility: RMS per sound can be
#' calculated as the mean / median / max of RMS values per frame.
#'
#' @seealso \code{\link{analyze}} \code{\link{getLoudness}}
#'
#' @inheritParams spectrogram
#' @inheritParams segment
#' @param stereo 'left' = only left channel, 'right' = only right channel,
#' 'average' = take the mean of the two channels, 'both' = return RMS for both
#' channels separately
#' @param killDC if TRUE, removed DC offset (see also \code{\link{flatEnv}})
#' @param normalize if TRUE, the RMS amplitude is returned as proportion of
#' the maximum possible amplitude as given by \code{scale}
#' @param windowDC the window for calculating DC offset, ms
#' @param plot if TRUE, plot a contour of RMS amplitude
#' @param xlab,ylab,main general graphical parameters
#' @param type,col,lwd graphical parameters pertaining to the RMS envelope
#' @param ... other graphical parameters
#'
#' @return Returns a list containing: \describe{
#' \item{$detailed: }{a list of RMS amplitudes per frame for each sound, on the scale of input; names give time stamps for the center of each frame, in ms.}
#' \item{$summary: }{a dataframe with summary measures, one row per sound}
#' }
#'
#' @export
#' @examples
#' s = soundgen() + .25 # with added DC offset
#' # osc(s)
#' r = getRMS(s, samplingRate = 16000, from = .05,
#' windowLength = 40, overlap = 50, killDC = TRUE,
#' plot = TRUE, type = 'l', lty = 2, main = 'RMS envelope')
#' r
#' # short window = jagged envelope
#' r = getRMS(s, samplingRate = 16000,
#' windowLength = 5, overlap = 0, killDC = TRUE,
#' plot = TRUE, col = 'blue', pch = 13, main = 'RMS envelope')
#'
#' # stereo
#' wave_stereo = tuneR::Wave(
#' left = runif(1000, -1, 1) * 16000,
#' right = runif(1000, -1, 1) / 3 * 16000,
#' bit = 16, samp.rate = 4000)
#' getRMS(wave_stereo)$summary
#' getRMS(wave_stereo, stereo = 'right')$summary
#' getRMS(wave_stereo, stereo = 'average')$summary
#' getRMS(wave_stereo, from = .05,
#' stereo = 'both', plot = TRUE)$summary
#'
#' \dontrun{
#' r = getRMS('~/Downloads/temp', savePlots = '~/Downloads/temp/plots')
#' r$summary
#'
#' # Compare:
#' analyze('~/Downloads/temp', pitchMethods = NULL,
#' plot = FALSE)$ampl_mean
#' # (per STFT frame, but should be very similar)
#'
#' User-defined summary functions:
#' ran = function(x) diff(range(x))
#' meanSD = function(x) {
#' paste0('mean = ', round(mean(x), 2), '; sd = ', round(sd(x), 2))
#' }
#' getRMS('~/Downloads/temp', summaryFun = c('mean', 'ran', 'meanSD'))$summary
#' }
getRMS = function(x,
samplingRate = NULL,
scale = NULL,
from = NULL,
to = NULL,
windowLength = 50,
step = NULL,
overlap = 70,
stereo = c('left', 'right', 'average', 'both')[1],
killDC = FALSE,
normalize = TRUE,
windowDC = 200,
summaryFun = 'mean',
reportEvery = NULL,
cores = 1,
plot = FALSE,
savePlots = NULL,
main = NULL,
xlab = '',
ylab = '',
type = 'b',
col = 'green',
lwd = 2,
width = 900,
height = 500,
units = 'px',
res = NA,
...) {
# match args
myPars = c(as.list(environment()), list(...))
# exclude some args
myPars = myPars[!names(myPars) %in% c(
'x', 'samplingRate', 'scale', 'from', 'to',
'savePlots', 'reportEvery', 'cores', 'summaryFun')]
pa = processAudio(x,
samplingRate = samplingRate,
scale = scale,
from = from,
to = to,
funToCall = '.getRMS',
savePlots = savePlots,
myPars = myPars,
reportEvery = reportEvery,
cores = cores
)
# htmlPlots
if (!is.null(pa$input$savePlots) && pa$input$n > 1) {
try(htmlPlots(pa$input, savePlots = savePlots, changesAudio = FALSE,
suffix = "rms", width = paste0(width, units)))
}
# prepare output
if (!is.null(summaryFun) && any(!is.na(summaryFun))) {
temp = vector('list', pa$input$n)
for (i in 1:pa$input$n) {
temp[[i]] = summarizeAnalyze(
data.frame(ampl = pa$result[[i]]),
summaryFun = summaryFun,
var_noSummary = NULL)
}
mysum_all = cbind(data.frame(file = pa$input$filenames_base),
do.call('rbind', temp))
} else {
mysum_all = NULL
}
if (pa$input$n == 1) pa$result = pa$result[[1]]
invisible(list(
detailed = pa$result,
summary = mysum_all
))
}
#' RMS amplitude per sound
#'
#' Internal soundgen function called by \code{\link{getRMS}}.
#' @param audio a list returned by \code{readAudio}
#' @inheritParams getRMS
#' @keywords internal
.getRMS = function(audio,
windowLength = 50,
step = NULL,
overlap = 75,
stereo = c('left', 'right', 'average', 'both')[1],
killDC = FALSE,
normalize = TRUE,
windowDC = 200,
plot = TRUE,
main = NULL,
xlab = '',
ylab = '',
type = 'b',
col = 'green',
lwd = 2,
width = 900,
height = 500,
units = 'px',
res = NA,
...) {
if (overlap < 0 | overlap > 100) {
warning('overlap must be >0 and <= 100%; resetting to 70')
overlap = 70
}
if (is.null(step)) step = windowLength * (1 - overlap / 100)
if (!is.numeric(windowLength) | windowLength <= 0 |
windowLength > (audio$duration * 1000)) {
windowLength = min(50, audio$duration / 2 * 1000)
warning(paste0('"windowLength" must be between 0 and sound_duration ms;
defaulting to ', windowLength, ' ms'))
}
windowLength_points = floor(windowLength / 1000 * audio$samplingRate / 2) * 2
step_points = round(step / 1000 * audio$samplingRate)
step = step_points / audio$samplingRate * 1000
windowLength = windowLength_points / audio$samplingRate * 1000
# step_points can only be an integer, introducing small timing errors in long sounds
if (stereo == 'right') audio$sound = audio$right
# DC offset
if (killDC) {
audio$sound = killDC(audio$sound, windowLength = windowDC,
samplingRate = audio$samplingRate)
if (!is.null(audio$right) & stereo %in% c('average', 'both'))
audio$right = killDC(audio$right, windowLength = windowDC,
samplingRate = audio$samplingRate)
}
# calculate RMS per frame
myseq = seq(1, max(1, (audio$ls - windowLength_points)), step_points)
r = apply(as.matrix(myseq), 1, function(x) {
sqrt(mean(audio$sound[x:(windowLength_points + x - 1)] ^ 2))
})
names(r) = myseq / audio$samplingRate * 1000 + windowLength / 2 + audio$timeShift * 1000
if (normalize) r = r / audio$scale
# same for the right channel
if (!is.null(audio$right) & stereo %in% c('average', 'both')) {
r2 = apply(as.matrix(myseq), 1, function(x) {
sqrt(mean(audio$right[x:(windowLength_points + x - 1)] ^ 2))
})
names(r2) = names(r)
if (normalize) r2 = r2 / audio$scale
}
if (stereo == 'average') r = (r + r2) / 2
if (stereo == 'both') r = list(left = r, right = r2)
# plotting
if (is.character(audio$savePlots)) {
plot = TRUE
png(filename = paste0(audio$savePlots, audio$filename_noExt, "_rms.png"),
width = width, height = height, units = units, res = res)
}
if (plot) {
if (is.null(main)) {
if (audio$filename_noExt == 'sound') {
main = ''
} else {
main = audio$filename_noExt
}
}
if (stereo != 'both') {
.osc(audio)
points(as.numeric(names(r)), r * audio$scale, type = type,
col = col, lwd = lwd, ...)
} else if (stereo == 'both' & !is.null(audio$right)) {
time = ((1:audio$ls) / audio$samplingRate + audio$timeShift) * 1000
op = par(c('mar', 'mfrow')) # save user's original pars
layout(matrix(c(2, 1), nrow = 2, byrow = TRUE))
par(mfrow = c(2, 1), mar = c(0, par()$mar[2:4]))
plot(time, audio$sound, type = 'n', main = main, xlab = xlab, ylab = ylab,
xaxt = 'n', ylim = c(-audio$scale, audio$scale), ...)
time_location = axTicks(1)
time_labels = convert_sec_to_hms(time_location / 1000, 3)
# axis(side = 1, at = time_location, labels = time_labels)
points(time, audio$sound, type = 'l')
points(as.numeric(names(r$left)), r$left * audio$scale, type = type,
col = col, lwd = lwd, ...)
text(0, audio$scale, adj = c(1, 1), labels = 'L', col = 'blue', cex = 1.5)
par(mar = c(op$mar[1:2], 0, op$mar[4]))
plot(time, audio$right, type = 'n', main = main, xlab = xlab, ylab = ylab,
xaxt = 'n', ylim = c(-audio$scale, audio$scale), ...)
time_location = axTicks(1)
time_labels = convert_sec_to_hms(time_location / 1000, 3)
axis(side = 1, at = time_location, labels = time_labels)
points(time, audio$right, type = 'l')
points(as.numeric(names(r$right)), r$right * audio$scale, type = type,
col = col, lwd = lwd, ...)
text(0, audio$scale, adj = c(1, 1), labels = 'R', col = 'blue', cex = 1.5)
# restore original pars
par('mar' = op$mar, 'mfrow' = op$mfrow)
}
if (is.character(audio$savePlots)) dev.off()
}
return(r)
}
#' Normalize folder
#'
#' Normalizes the amplitude of all wav/mp3 files in a folder based on their peak
#' or RMS amplitude or subjective loudness. This is good for playback
#' experiments, which require that all sounds should have similar intensity or
#' loudness.
#'
#' Algorithm: first all files are rescaled to have the same peak amplitude of
#' \code{maxAmp} dB. If \code{type = 'peak'}, the process ends here. If
#' \code{type = 'rms'}, there are two additional steps. First the original RMS
#' amplitude of all files is calculated per frame by \code{\link{getRMS}}. The
#' "quietest" sound with the lowest summary RMS value is not modified, so its
#' peak amplitude remains \code{maxAmp} dB. All the remaining sounds are
#' rescaled linearly, so that their summary RMS values becomes the same as that
#' of the "quietest" sound, and their peak amplitudes become smaller,
#' \code{<maxAmp}. Finally, if \code{type = 'loudness'}, the subjective
#' loudness of each sound is estimated by \code{\link{getLoudness}}, which
#' assumes frequency sensitivity typical of human hearing. The following
#' normalization procedure is similar to that for \code{type = 'rms'}.
#'
#' @seealso \code{\link{getRMS}} \code{\link{analyze}} \code{\link{getLoudness}}
#'
#' @inheritParams getRMS
#' @param myfolder full path to folder containing input audio files
#' @param type normalize so the output files has the same peak amplitude
#' ('peak'), root mean square amplitude ('rms'), or subjective loudness in
#' sone ('loudness')
#' @param maxAmp maximum amplitude in dB (0 = max possible, -10 = 10 dB below
#' max possible, etc.)
#' @param summaryFun should the output files have the same mean / median / max
#' etc rms amplitude or loudness? (summaryFun has no effect if type = 'peak')
#' @param saveAudio full path to where the normalized files should be saved
#' (defaults to 'myfolder/normalized')
#' @export
#' @examples
#' \dontrun{
#' # put a few short audio files in a folder, eg '~/Downloads/temp'
#' getRMS('~/Downloads/temp2', summaryFun = 'mean')$summary # different
#' normalizeFolder('~/Downloads/temp2', type = 'rms', summaryFun = 'mean',
#' saveAudio = '~/Downloads/temp2/normalized')
#' getRMS('~/Downloads/temp2/normalized', summaryFun = 'mean')$summary # same
#' # If the saved audio files are treated as stereo with one channel missing,
#' # try reconverting with ffmpeg (saving is handled by tuneR::writeWave)
#' }
normalizeFolder = function(
myfolder,
type = c('peak', 'rms', 'loudness')[1],
maxAmp = 0,
summaryFun = 'mean',
windowLength = 50,
step = NULL,
overlap = 70,
killDC = FALSE,
windowDC = 200,
saveAudio = NULL,
reportEvery = NULL
) {
time_start = proc.time() # timing
filenames = list.files(myfolder, pattern = "*.wav|.mp3|.WAV|.MP3",
full.names = TRUE)
filesizes = file.info(filenames)$size
if (length(filenames) < 1) {
stop(paste('No wav/mp3 files found in', myfolder))
}
# in order to provide more accurate estimates of time to completion,
# check the size of all files in the target folder
n = length(filenames)
## load all the files
print('Loading...')
files = vector('list', n)
for (i in 1:n) {
ext = substr(filenames[i], (nchar(filenames[i]) - 2), nchar(filenames[i]))
if (ext %in% c('wav', 'WAV')) {
files[[i]] = tuneR::readWave(filenames[i])
} else if (ext %in% c('mp3', 'MP3')) {
files[[i]] = tuneR::readMP3(filenames[i])
} else {
stop('Input not recognized')
}
}
## process all the files
print('Processing...')
# for either peak or RMS normalization, start by peak normalization to maxAmp dB
level = 10 ^ (maxAmp / 20)
for (i in 1:n) {
files[[i]] = tuneR::normalize(files[[i]],
unit = as.character(files[[i]]@bit),
rescale = TRUE, level = level)
}
# for RMS- or loudness-normalization, perform additional steps
if (type %in% c('rms', 'loudness')) {
perSound = vector('list', n)
if (type == 'rms') {
for (i in 1:n) {
# calculate the RMS amplitude of each file
perSound[[i]] = getRMS(files[[i]],
windowLength = windowLength,
step = step,
overlap = overlap,
scale = 2^(files[[i]]@bit - 1),
killDC = killDC,
windowDC = windowDC,
plot = FALSE)$detailed
}
} else if (type == 'loudness') {
for (i in 1:n) {
# estimate subjective loudness of each file
perSound[[i]] = getLoudness(files[[i]],
scale = 2^(files[[i]]@bit - 1),
windowLength = windowLength,
step = step,
plot = FALSE)$loudness
}
}
# summary measure per file
summaryPerSound = unlist(lapply(perSound, summaryFun))
names(summaryPerSound) = basename(filenames)
# find the quietest file
ref = which.min(summaryPerSound)
# the quietest file is untouched, but all others are rescaled to have the
# same RMS/loudness as the quietest one
for (i in 1:n) {
if (i != ref) {
if (type == 'rms') {
rescale = summaryPerSound[ref] / summaryPerSound[i]
} else if (type == 'loudness') {
rescale = (summaryPerSound[ref] / summaryPerSound[i]) ^ (5 / 3)
}
files[[i]]@left = as.integer(round(files[[i]]@left * rescale))
}
}
}
# save the rescaled files
if (is.null(saveAudio)) saveAudio = paste0(myfolder, '/normalized')
if (!is.na(saveAudio)) {
print('Saving...')
if (!dir.exists(saveAudio)) dir.create(saveAudio)
for (i in 1:n) {
file_wo_ext = sub("([^.]+)\\.[[:alnum:]]+$", "\\1", basename(filenames[i]))
tuneR::writeWave(
files[[i]],
filename = paste0(saveAudio, '/', file_wo_ext, '.wav')
)
}
}
# report time
if (is.null(reportEvery) || is.finite(reportEvery)) {
reportTime(i = n, nIter = n, reportEvery = reportEvery,
time_start = time_start, jobs = filesizes)
}
}
#' Flat envelope / compressor
#'
#' Applies a compressor - that is, flattens the amplitude envelope of a
#' waveform, reducing the difference in amplitude between loud and quiet
#' sections. This is achieved by dividing the waveform by some function of its
#' smoothed amplitude envelope (Hilbert, peak or root mean square).
#'
#' @inheritParams spectrogram
#' @inheritParams segment
#' @param compression the amount of compression to apply: 0 = none, 1 = maximum
#' @param method hil = Hilbert envelope, rms = root mean square amplitude, peak
#' = peak amplitude per window
#' @param windowLength the length of smoothing window, ms
#' @param windowLength_points the length of smoothing window, points. If
#' specified, overrides \code{windowLength}
#' @param killDC if TRUE, dynamically removes DC offset or similar deviations of
#' average waveform from zero (see examples)
#' @param dynamicRange parts of sound quieter than \code{-dynamicRange} dB will
#' not be amplified
#' @param plot if TRUE, plots the original sound, the smoothed envelope, and
#' the compressed sound
#' @param saveAudio full path to the folder in which to save the compressed
#' sound(s)
#' @param col the color of amplitude contours
#' @param ... other graphical parameters passed to \code{points()} that control
#' the appearance of amplitude contours, eg \code{lwd, lty}, etc.
#'
#' @return If the input is a single audio (file, Wave, or numeric vector),
#' returns the compressed waveform as a numeric vector with the original
#' sampling rate and scale. If the input is a folder with several audio files,
#' returns a list of compressed waveforms, one for each file.
#' @export
#' @examples
#' a = rnorm(500) * seq(1, 0, length.out = 500)
#' b = flatEnv(a, 1000, plot = TRUE, windowLength_points = 5) # too short
#' c = flatEnv(a, 1000, plot = TRUE, windowLength_points = 450) # too long
#' d = flatEnv(a, 1000, plot = TRUE, windowLength_points = 100) # about right
#'
#' \dontrun{
#' s = soundgen(sylLen = 1000, ampl = c(0, -40, 0), plot = TRUE)
#' # playme(s)
#' s_flat1 = flatEnv(s, 16000, dynamicRange = 60, plot = TRUE,
#' windowLength = 50, method = 'hil')
#' s_flat2 = flatEnv(s, 16000, dynamicRange = 60, plot = TRUE,
#' windowLength = 10, method = 'rms')
#' s_flat3 = flatEnv(s, 16000, dynamicRange = 60, plot = TRUE,
#' windowLength = 10, method = 'peak')
#' # playme(s_flat2)
#'
#' # Remove DC offset
#' s1 = c(rep(0, 50), runif(1000, -1, 1), rep(0, 50)) +
#' seq(.3, 1, length.out = 1100)
#' s2 = flatEnv(s1, 16000, plot = TRUE, windowLength_points = 50, killDC = FALSE)
#' s3 = flatEnv(s1, 16000, plot = TRUE, windowLength_points = 50, killDC = TRUE)
#'
#' # Compress and save all audio files in a folder
#' s4 = flatEnv('~/Downloads/temp2',
#' method = 'peak', compression = .5,
#' saveAudio = '~/Downloads/temp2/compressed',
#' savePlots = '~/Downloads/temp2/compressed',
#' col = 'green', lwd = 5)
#' osc(s4[[1]])
#' }
flatEnv = function(x,
samplingRate = NULL,
scale = NULL,
compression = 1,
method = c('hil', 'rms', 'peak')[1],
windowLength = 50,
windowLength_points = NULL,
killDC = FALSE,
dynamicRange = 40,
reportEvery = NULL,
cores = 1,
saveAudio = NULL,
plot = FALSE,
savePlots = NULL,
col = 'blue',
width = 900,
height = 500,
units = 'px',
res = NA,
...) {
# match args
myPars = c(as.list(environment()), list(...))
# exclude some args
myPars = myPars[!names(myPars) %in% c(
'x', 'samplingRate', 'scale', 'reportEvery', 'cores', 'savePlots', 'saveAudio')]
pa = processAudio(x = x,
samplingRate = samplingRate,
scale = scale,
saveAudio = saveAudio,
savePlots = savePlots,
funToCall = '.flatEnv',
myPars = myPars,
reportEvery = reportEvery,
cores = cores)
# htmlPlots
if (!is.null(pa$input$savePlots) && pa$input$n > 1) {
try(htmlPlots(pa$input, savePlots = savePlots, changesAudio = TRUE,
suffix = "compressor", width = paste0(width, units)))
}
# prepare output
if (pa$input$n == 1) {
result = pa$result[[1]]
} else {
result = pa$result
}
invisible(result)
}
#' @rdname flatEnv
#' @export
compressor = flatEnv
#' Flat envelope per sound
#'
#' Internal soundgen function
#'
#' @param audio a list returned by \code{readAudio}
#' @inheritParams flatEnv
#' @keywords internal
.flatEnv = function(audio,
compression = 1,
method = c('hil', 'rms', 'peak')[1],
windowLength = 50,
windowLength_points = NULL,
killDC = FALSE,
dynamicRange = 40,
plot = FALSE,
col = 'blue',
width = 900,
height = 500,
units = 'px',
res = NA,
...) {
if (!is.numeric(windowLength_points)) {
if (is.numeric(windowLength)) {
if (is.numeric(audio$samplingRate)) {
windowLength_points = windowLength / 1000 * audio$samplingRate
} else {
stop(paste(
'Please specify either windowLength (ms) plus samplingRate (Hz)',
'or the length of smoothing window in points (windowLength_points)'))
}
}
}
# audio$scale = original scale (eg -1 to +1 gives m = 1)
throwaway_lin = 10 ^ (-dynamicRange / 20) * audio$scale
# from dB to linear + normalize
# remove DC offset
if (killDC) {
soundFlat = killDC(sound = audio$sound,
windowLength_points = windowLength_points,
plot = FALSE)
} else {
soundFlat = audio$sound
}
# get smoothed amplitude envelope
env = getEnv(sound = soundFlat,
windowLength_points = windowLength_points,
method = method)
env[env < 0] = 0
# don't amplify very quiet sections
idx = which(env > throwaway_lin)
# flatten amplitude envelope
soundFlat[idx] = soundFlat[idx] * (1 - compression) +
soundFlat[idx] / env[idx] * audio$scale * compression
# re-normalize to original scale
soundFlat = soundFlat / max(abs(soundFlat))
if (!is.null(audio$scale_used))
soundFlat = soundFlat * audio$scale_used
# PLOTTING
if (is.character(audio$savePlots)) {
plot = TRUE
png(filename = paste0(audio$savePlots, audio$filename_noExt, "_compressor.png"),
width = width, height = height, units = units, res = res)
}
if (plot) {
ylim = range(c(audio$sound, soundFlat))
op = par('mfrow')
par(mfrow = c(1, 2))
plot(audio$sound, type = 'l', main = 'Original', ylim = ylim)
points(env, type = 'l', col = col, ...)
env_new = getEnv(sound = soundFlat,
windowLength_points = windowLength_points,
method = method)
plot(soundFlat, type = 'l', main = 'Compressed', ylim = ylim)
points(env_new, type = 'l', col = col, ...)
par(mfrow = op)
if (is.character(audio$savePlots)) dev.off()
}
if (!is.null(audio$saveAudio)) {
if (!dir.exists(audio$saveAudio)) dir.create(audio$saveAudio)
filename = paste0(audio$saveAudio, '/', audio$filename_noExt, '.wav')
writeAudio(soundFlat, audio = audio, filename = filename)
}
return(soundFlat)
}
#' Transplant envelope
#'
#' Extracts a smoothed amplitude envelope of the \code{donor} sound and applies
#' it to the \code{recipient} sound. Both sounds are provided as numeric
#' vectors; they can differ in length and sampling rate. Note that the result
#' depends on the amount of smoothing (controlled by \code{windowLength}) and
#' the chosen method of calculating the envelope. Very similar to
#' \code{\link[seewave]{setenv}}, but with a different smoothing algorithm and
#' with a choice of several types of envelope: hil, rms, or peak.
#'
#' @seealso \code{\link{flatEnv}} \code{\link[seewave]{setenv}}
#'
#' @param donor the sound that "donates" the amplitude envelope
#' @param recipient the sound that needs to have its amplitude envelope adjusted
#' @param samplingRateD,samplingRateR sampling rate of the donor and recipient,
#' respectively (only needed for vectors, not files)
#' @inheritParams flatEnv
#' @return Returns the recipient sound with the donor's amplitude envelope - a
#' numeric vector with the same sampling rate as the recipient
#' @export
#' @examples
#' donor = rnorm(500) * seq(1, 0, length.out = 500)
#' recipient = soundgen(sylLen = 600, addSilence = 50)
#' transplantEnv(donor, samplingRateD = 200,
#' recipient, samplingRateR = 16000,
#' windowLength = 50, method = 'hil', plot = TRUE)
#' transplantEnv(donor, samplingRateD = 200,
#' recipient, samplingRateR = 16000,
#' windowLength = 10, method = 'peak', plot = TRUE)
transplantEnv = function(donor,
samplingRateD = NULL,
recipient,
samplingRateR = samplingRateD,
windowLength = 50,
method = c('hil', 'rms', 'peak')[3],
killDC = FALSE,
dynamicRange = 80,
plot = FALSE) {
# Read inputs
donor = readAudio(
donor,
input = checkInputType(donor),
samplingRate = samplingRateD
)
recipient = readAudio(
recipient,
input = checkInputType(recipient),
samplingRate = samplingRateR
)
windowLength_points_donor = windowLength / 1000 * donor$samplingRate
windowLength_points_recip = windowLength / 1000 * recipient$samplingRate
throwaway_lin = 10 ^ (-dynamicRange / 20) * recipient$scale
# get the amplitude envelope of the recipient
env_recipient = getEnv(sound = recipient$sound,
windowLength_points = windowLength_points_recip,
method = method)
len_recipient = length(env_recipient)
# don't amplify very quiet sections
env_recip_cut = env_recipient
env_recip_cut[env_recip_cut < throwaway_lin] = throwaway_lin
# plot(env_recip_cut, type = 'l')
# get the amplitude envelope of the donor
env_donor = getEnv(sound = donor$sound,
windowLength_points = windowLength_points_donor,
method = method)
env_donor1 = env_donor / max(env_donor) # normalize
env_donor1 = approx(env_donor1, n = len_recipient)$y
# plot(env_donor1, type = 'l')
# flatten the envelope of the recipient and apply the donor's envelope
out = recipient$sound / env_recip_cut * env_donor1
out = out / max(abs(out)) * recipient$scale
# plot(out, type = 'l')
if (plot) {
len_donor = length(env_donor)
max_donor = max(abs(donor$sound))
max_recipient = max(c(abs(recipient$sound), abs(out)))
op = par('mfrow')
par(mfrow = c(1, 3))
.osc(donor, main = 'Donor', ylim = c(-max_donor, max_donor),
xlab = '', ylab = 'Amplitude', midline = FALSE)
par(new = TRUE)
.osc(list(sound = env_donor,
samplingRate = donor$samplingRate,
ls = len_donor,
filename_noExt = ''),
lty = 1, col = 'blue', xlab = '', ylab = '', midline = FALSE,
xaxt = 'n', yaxt = 'n', ylim = c(-max_donor, max_donor))
.osc(recipient, main = 'Recipient', ylim = c(-max_recipient, max_recipient),
xlab = 'Time, s', ylab = '', midline = FALSE)
par(new = TRUE)
.osc(list(sound = env_recipient,
samplingRate = recipient$samplingRate,
ls = len_recipient,
filename_noExt = ''),
lty = 1, col = 'blue', xlab = '', ylab = '', midline = FALSE,
xaxt = 'n', yaxt = 'n', ylim = c(-max_recipient, max_recipient))
.osc(list(sound = out,
samplingRate = recipient$samplingRate,
ls = len_recipient,
filename_noExt = ''),
main = 'Output', ylim = c(-max_recipient, max_recipient),
xlab = '', ylab = '', midline = FALSE)
par(new = TRUE)
.osc(list(
sound = getEnv(out, windowLength_points_recip, method),
samplingRate = recipient$samplingRate,
ls = len_recipient,
filename_noExt = ''),
lty = 1, col = 'blue', xlab = '', ylab = '', midline = FALSE,
xaxt = 'n', yaxt = 'n', ylim = c(-max_recipient, max_recipient))
par(mfrow = op)
}
invisible(out)
}
#' Add amplitude modulation
#'
#' Adds sinusoidal or logistic amplitude modulation to a sound. This produces
#' additional harmonics in the spectrum at ±am_freq around each original
#' harmonic and makes the sound rough. The optimal frequency for creating a
#' perception of roughness is ~70 Hz (Fastl & Zwicker "Psychoacoustics").
#' Sinusoidal AM creates a single pair of new harmonics, while non-sinusoidal AM
#' creates more extra harmonics (see examples).
#' @inheritParams spectrogram
#' @inheritParams soundgen
#' @param play if TRUE, plays the processed audio
#' @param saveAudio full (!) path to folder for saving the processed audio; NULL
#' = don't save, '' = same as input folder (NB: overwrites the originals!)
#' @param plot if TRUE, plots the amplitude modulation
#' @export
#' @examples
#' sound1 = soundgen(pitch = c(200, 300), addSilence = 0)
#' s1 = addAM(sound1, 16000, amDep = c(0, 50, 0), amFreq = 75, plot = TRUE)
#' # playme(s1)
#' \dontrun{
#' # Parameters can be specified as in the soundgen() function, eg:
#' s2 = addAM(sound1, 16000,
#' amDep = list(time = c(0, 50, 52, 200, 201, 300),
#' value = c(0, 0, 35, 25, 0, 0)),
#' plot = TRUE, play = TRUE)
#'
#' # Sinusoidal AM produces exactly 2 extra harmonics at ±am_freq
#' # around each f0 harmonic:
#' s3 = addAM(sound1, 16000, amDep = 30, amFreq = c(50, 80),
#' amType = 'sine', plot = TRUE, play = TRUE)
#' spectrogram(s3, 16000, windowLength = 150, ylim = c(0, 2))
#'
#' # Non-sinusoidal AM produces multiple new harmonics,
#' # which can resemble subharmonics...
#' s4 = addAM(sound1, 16000, amDep = 70, amFreq = 50, amShape = -1,
#' plot = TRUE, play = TRUE)
#' spectrogram(s4, 16000, windowLength = 150, ylim = c(0, 2))
#'
#' # ...but more often look like sidebands
#' sound3 = soundgen(sylLen = 600, pitch = c(800, 1300, 1100), addSilence = 0)
#' s5 = addAM(sound3, 16000, amDep = c(0, 30, 100, 40, 0),
#' amFreq = 105, amShape = -.3,
#' plot = TRUE, play = TRUE)
#' spectrogram(s5, 16000, ylim = c(0, 5))
#'
#' # Feel free to add AM stochastically:
#' s6 = addAM(sound1, 16000,
#' amDep = rnorm(10, 40, 20), amFreq = rnorm(20, 70, 20),
#' plot = TRUE, play = TRUE)
#' spectrogram(s6, 16000, windowLength = 150, ylim = c(0, 2))
#'
#' # If am_freq is locked to an integer ratio of f0, we can get subharmonics
#' # For ex., here is with pitch 400-600-400 Hz (soundgen interpolates pitch
#' # on a log scale and am_freq on a linear scale, so we align them by extracting
#' # a long contour on a log scale for both)
#' con = getSmoothContour(anchors = c(400, 600, 400),
#' len = 20, thisIsPitch = TRUE)
#' s = soundgen(sylLen = 1500, pitch = con, amFreq = con/3, amDep = 30,
#' plot = TRUE, play = TRUE, ylim = c(0, 3))
#'
#' # Process all files in a folder and save the modified audio
#' addAM('~/Downloads/temp', saveAudio = '~/Downloads/temp/AM',
#' amFreq = 70, amDep = c(0, 50))
#' }
addAM = function(x,
samplingRate = NULL,
amDep = 25,
amFreq = 30,
amType = c('logistic', 'sine')[1],
amShape = 0,
invalidArgAction = c('adjust', 'abort', 'ignore')[1],
plot = FALSE,
play = FALSE,
saveAudio = NULL,
reportEvery = NULL,
cores = 1) {
# check the format of AM pars
anchors = c('amDep', 'amFreq', 'amShape')
for (anchor in anchors) {
assign(anchor, reformatAnchors(get(anchor)))
}
rm('anchor', 'anchors')
# match args
myPars = c(as.list(environment()))
# exclude some args
myPars = myPars[!names(myPars) %in% c(
'x', 'samplingRate', 'reportEvery', 'cores', 'saveAudio')]
pa = processAudio(x,
samplingRate = samplingRate,
saveAudio = saveAudio,
funToCall = '.addAM',
myPars = myPars,
reportEvery = reportEvery,
cores = cores)
# prepare output
if (pa$input$n == 1) {
result = pa$result[[1]]
} else {
result = pa$result
}
invisible(result)
}
#' Add AM to a sound
#'
#' Internal soundgen function, see \code{\link{addAM}}.
#'
#' @inheritParams addAM
#' @param audio a list returned by \code{readAudio}
#' @keywords internal
.addAM = function(
audio,
amDep = 25,
amFreq = 30,
amType = c('logistic', 'sine')[1],
amShape = 0,
invalidArgAction = c('adjust', 'abort', 'ignore')[1],
plot = FALSE,
play = FALSE
) {
# vectorize
amPar_vect = c('amDep', 'amFreq', 'amShape')
# just to get rid of of NOTE on CRAN:
amDep_vector = amFreq_vector = amShape_vector = vector()
for (p in amPar_vect) {
p_unique_value = unique(get(p)$value)
if (length(p_unique_value) > 1) {
if (invalidArgAction == 'ignore') {
valueFloor_p = valueCeiling_p = NULL
} else {
valueFloor_p = permittedValues[p, 'low']
valueCeiling_p = permittedValues[p, 'high']
}
p_vectorized = getSmoothContour(
anchors = get(p),
len = audio$ls,
interpol = 'approx',
valueFloor = valueFloor_p,
valueCeiling = valueCeiling_p
)
# plot(p_vectorized, type = 'l')
assign(paste0(p, '_vector'), p_vectorized)
} else {
assign(paste0(p, '_vector'), p_unique_value)
}
}
# prepare am vector
if (amType == 'sine') {
if (length(amFreq_vector) == 1) {
int = amFreq_vector * (1:audio$ls)
} else {
int = cumsum(amFreq_vector)
}
sig = .5 + .5 * cos(2 * pi * int / audio$samplingRate)
} else {
sig = getSigmoid(len = audio$ls,
samplingRate = audio$samplingRate,
freq = amFreq_vector,
shape = amShape_vector)
}
# plot(sig, type = 'l')
# sig is on a scale [0, 1]
am = 1 - sig * amDep_vector / 100
sound_am = audio$sound * am
if (plot) {
.osc(list(sound = am,
samplingRate = audio$samplingRate,
ls = length(am)),
main = 'Amplitude modulation',
xlab = 'Time, ms',
ylab = '',
ylim = c(0, 1),
midline = FALSE)
}
if (play) playme(sound_am, audio$samplingRate)
if (is.character(audio$saveAudio)) {
filename = paste0(audio$saveAudio, '/', audio$filename_noExt, '.wav')
writeAudio(sound_am, audio = audio, filename = filename)
}
invisible(sound_am)
}
#' Get amplitude envelope
#'
#' Returns the smoothed amplitude envelope of a waveform on the original scale.
#' Unlike seewave::env, this function always returns an envelope of the same
#' length as the original sound, regardless of the amount of smoothing.
#' @param sound numeric vector
#' @param windowLength_points the length of smoothing window, in points
#' @param method 'peak' for peak amplitude per window, 'rms' for root mean
#' square amplitude, 'mean' for mean (for DC offset removal), 'hil' for
#' Hilbert, 'raw' for low-pass filtering the actual sound
#' @export
#' @examples
#' a = rnorm(500) * seq(1, 0, length.out = 500)
#' windowLength_points = 50
#' scale = max(abs(a))
#' plot(a, type = 'l', ylim = c(-scale, scale))
#' points(soundgen:::getEnv(a, windowLength_points, 'rms'),
#' type = 'l', col = 'red')
#' points(soundgen:::getEnv(a, windowLength_points, 'peak'),
#' type = 'l', col = 'green')
#' points(soundgen:::getEnv(a, windowLength_points, 'hil'),
#' type = 'l', col = 'blue')
#' points(soundgen:::getEnv(a, windowLength_points, 'mean'),
#' type = 'l', lty = 3, lwd = 3)
getEnv = function(
sound,
windowLength_points,
method = c('rms', 'hil', 'peak', 'raw', 'mean')[1]
) {
if (!method %in% c('rms', 'hil', 'peak', 'raw', 'mean'))
stop("Valid values for method are c('rms', 'hil', 'peak', 'mean')")
windowLength_points = round(windowLength_points)
sound = c(rep(0, windowLength_points),
sound,
rep(0, windowLength_points))
len = length(sound)
if (method == 'peak') sound_abs = abs(sound) # avoid repeated calculations
# moving window operations: get per window, then upsample
if (windowLength_points >= len / 2) {
# short sound relative to window - just take beginning and end (2 points)
s = c(1, len)
} else {
s = c(1,
seq(from = floor(windowLength_points / 2),
to = length(sound) - floor(windowLength_points / 2),
by = windowLength_points),
length(sound))
}
# s is a sequence of starting indices for windows over which we average
envShort = rep(NA, length(s) - 1)
for (i in 1:(length(s) - 1)) {
seg = s[i] : s[i+1]
if (method == 'peak') {
# get moving peak amplitude
envShort[i] = max(sound_abs[seg])
} else if (method == 'mean' || method == 'raw') {
envShort[i] = mean(sound[seg])
} else if (method == 'rms') {
envShort[i] = mean(sqrt(sound[seg] ^ 2))
} else if (method == 'hil') {
envShort[i] = mean(Mod(seewave::hilbert(sound[seg],
f = 1, # not actually needed
fftw = FALSE)))
}
}
# upsample and smooth
env = getSmoothContour(
anchors = data.frame(time = seq(0, 1, length.out = length(envShort)),
value = envShort),
len = length(sound),
valueFloor = 0,
loessSpan = 1
)
# remove the zero-padded regions
env = env[(windowLength_points + 1):(len - windowLength_points)]
# osc(env)
return(env)
}
#' Kill DC
#'
#' Removes DC offset or similar disbalance in a waveform dynamically, by
#' subtracting a smoothed ~moving average. Simplified compared to a true moving
#' average, but very fast (a few ms per second of 44100 audio).
#' @inheritParams flatEnv
#' @param plot if TRUE, plots the original sound, smoothed moving average, and
#' modified sound
#' @keywords internal
#' @examples
#' # remove static DC offset
#' a = rnorm(500) + .3
#' b = soundgen:::killDC(a, windowLength_points = 500, plot = TRUE)
#'
#' # remove trend
#' a = rnorm(500) + seq(0, 1, length.out = 500)
#' b = soundgen:::killDC(a, windowLength_points = 100, plot = TRUE)
#'
#' # can also be used as a high-pass filter
#' a = rnorm(500) + sin(1:500 / 50)
#' b = soundgen:::killDC(a, windowLength_points = 25, plot = TRUE)
killDC = function(sound,
windowLength = 200,
samplingRate = 16000,
windowLength_points = NULL,
plot = FALSE) {
if (!is.numeric(windowLength_points)) {
if (is.numeric(windowLength)) {
if (is.numeric(samplingRate)) {
windowLength_points = windowLength / 1000 * samplingRate
} else {
stop(paste('Please specify either windowLength (ms) plus samplingRate (Hz)',
'or the length of smoothing window in points (windowLength_points)'))
}
}
}
env = getEnv(sound = sound,
windowLength_points = windowLength_points,
method = 'mean')
soundNorm = sound - env
if (plot) {
op = par('mfrow')
par(mfrow = c(1, 2))
plot(sound, type = 'l', main = 'Original')
points(env, type = 'l', lty = 1, col = 'blue')
points(rep(0, length(sound)), type = 'l', lty = 2)
plot(soundNorm, type = 'l', main = 'Env removed')
points(rep(0, length(sound)), type = 'l', col = 'blue')
par(mfrow = op)
}
return(soundNorm)
}
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Defines functions killDC getEnv .addAM addAM transplantEnv .flatEnv flatEnv normalizeFolder .getRMS getRMS
Documented in addAM .addAM flatEnv .flatEnv getEnv getRMS .getRMS killDC normalizeFolder transplantEnv
#' RMS amplitude
#'
#' Calculates root mean square (RMS) amplitude in overlapping windows, providing
#' an envelope of RMS amplitude - a measure of sound intensity. Longer windows
#' provide smoother, more robust estimates; shorter windows and more overlap
#' improve temporal resolution, but they also increase processing time and make
#' the contour less smooth.
#'
#' Note that you can also get similar estimates per frame from
#' \code{\link{analyze}} on a normalized scale of 0 to 1, but \code{getRMS} is
#' much faster, operates on the original scale, and plots the amplitude contour.
#' If you need RMS for the entire sound instead of per frame, you can simply
#' calculate it as \code{sqrt(mean(x^2))}, where \code{x} is your waveform.
#' Having RMS estimates per frame gives more flexibility: RMS per sound can be
#' calculated as the mean / median / max of RMS values per frame.
#'
#' @seealso \code{\link{analyze}} \code{\link{getLoudness}}
#'
#' @inheritParams spectrogram
#' @inheritParams segment
#' @param stereo 'left' = only left channel, 'right' = only right channel,
#' 'average' = take the mean of the two channels, 'both' = return RMS for both
#' channels separately
#' @param killDC if TRUE, removed DC offset (see also \code{\link{flatEnv}})
#' @param normalize if TRUE, the RMS amplitude is returned as proportion of
#' the maximum possible amplitude as given by \code{scale}
#' @param windowDC the window for calculating DC offset, ms
#' @param plot if TRUE, plot a contour of RMS amplitude
#' @param xlab,ylab,main general graphical parameters
#' @param type,col,lwd graphical parameters pertaining to the RMS envelope
#' @param ... other graphical parameters
#'
#' @return Returns a list containing: \describe{
#' \item{$detailed: }{a list of RMS amplitudes per frame for each sound, on the scale of input; names give time stamps for the center of each frame, in ms.}
#' \item{$summary: }{a dataframe with summary measures, one row per sound}
#' }
#'
#' @export
#' @examples
#' s = soundgen() + .25 # with added DC offset
#' # osc(s)
#' r = getRMS(s, samplingRate = 16000, from = .05,
#' windowLength = 40, overlap = 50, killDC = TRUE,
#' plot = TRUE, type = 'l', lty = 2, main = 'RMS envelope')
#' r
#' # short window = jagged envelope
#' r = getRMS(s, samplingRate = 16000,
#' windowLength = 5, overlap = 0, killDC = TRUE,
#' plot = TRUE, col = 'blue', pch = 13, main = 'RMS envelope')
#'
#' # stereo
#' wave_stereo = tuneR::Wave(
#' left = runif(1000, -1, 1) * 16000,
#' right = runif(1000, -1, 1) / 3 * 16000,
#' bit = 16, samp.rate = 4000)
#' getRMS(wave_stereo)$summary
#' getRMS(wave_stereo, stereo = 'right')$summary
#' getRMS(wave_stereo, stereo = 'average')$summary
#' getRMS(wave_stereo, from = .05,
#' stereo = 'both', plot = TRUE)$summary
#'
#' \dontrun{
#' r = getRMS('~/Downloads/temp', savePlots = '~/Downloads/temp/plots')
#' r$summary
#'
#' # Compare:
#' analyze('~/Downloads/temp', pitchMethods = NULL,
#' plot = FALSE)$ampl_mean
#' # (per STFT frame, but should be very similar)
#'
#' User-defined summary functions:
#' ran = function(x) diff(range(x))
#' meanSD = function(x) {
#' paste0('mean = ', round(mean(x), 2), '; sd = ', round(sd(x), 2))
#' }
#' getRMS('~/Downloads/temp', summaryFun = c('mean', 'ran', 'meanSD'))$summary
#' }
getRMS = function(x,
samplingRate = NULL,
scale = NULL,
from = NULL,
to = NULL,
windowLength = 50,
step = NULL,
overlap = 70,
stereo = c('left', 'right', 'average', 'both')[1],
killDC = FALSE,
normalize = TRUE,
windowDC = 200,
summaryFun = 'mean',
reportEvery = NULL,
cores = 1,
plot = FALSE,
savePlots = NULL,
main = NULL,
xlab = '',
ylab = '',
type = 'b',
col = 'green',
lwd = 2,
width = 900,
height = 500,
units = 'px',
res = NA,
...) {
# match args
myPars = c(as.list(environment()), list(...))
# exclude some args
myPars = myPars[!names(myPars) %in% c(
'x', 'samplingRate', 'scale', 'from', 'to',
'savePlots', 'reportEvery', 'cores', 'summaryFun')]
pa = processAudio(x,
samplingRate = samplingRate,
scale = scale,
from = from,
to = to,
funToCall = '.getRMS',
savePlots = savePlots,
myPars = myPars,
reportEvery = reportEvery,
cores = cores
)
# htmlPlots
if (!is.null(pa$input$savePlots) && pa$input$n > 1) {
try(htmlPlots(pa$input, savePlots = savePlots, changesAudio = FALSE,
suffix = "rms", width = paste0(width, units)))
}
# prepare output
if (!is.null(summaryFun) && any(!is.na(summaryFun))) {
temp = vector('list', pa$input$n)
for (i in 1:pa$input$n) {
temp[[i]] = summarizeAnalyze(
data.frame(ampl = pa$result[[i]]),
summaryFun = summaryFun,
var_noSummary = NULL)
}
mysum_all = cbind(data.frame(file = pa$input$filenames_base),
do.call('rbind', temp))
} else {
mysum_all = NULL
}
if (pa$input$n == 1) pa$result = pa$result[[1]]
invisible(list(
detailed = pa$result,
summary = mysum_all
))
}
#' RMS amplitude per sound
#'
#' Internal soundgen function called by \code{\link{getRMS}}.
#' @param audio a list returned by \code{readAudio}
#' @inheritParams getRMS
#' @keywords internal
.getRMS = function(audio,
windowLength = 50,
step = NULL,
overlap = 75,
stereo = c('left', 'right', 'average', 'both')[1],
killDC = FALSE,
normalize = TRUE,
windowDC = 200,
plot = TRUE,
main = NULL,
xlab = '',
ylab = '',
type = 'b',
col = 'green',
lwd = 2,
width = 900,
height = 500,
units = 'px',
res = NA,
...) {
if (overlap < 0 | overlap > 100) {
warning('overlap must be >0 and <= 100%; resetting to 70')
overlap = 70
}
if (is.null(step)) step = windowLength * (1 - overlap / 100)
if (!is.numeric(windowLength) | windowLength <= 0 |
windowLength > (audio$duration * 1000)) {
windowLength = min(50, audio$duration / 2 * 1000)
warning(paste0('"windowLength" must be between 0 and sound_duration ms;
defaulting to ', windowLength, ' ms'))
}
windowLength_points = floor(windowLength / 1000 * audio$samplingRate / 2) * 2
step_points = round(step / 1000 * audio$samplingRate)
step = step_points / audio$samplingRate * 1000
windowLength = windowLength_points / audio$samplingRate * 1000
# step_points can only be an integer, introducing small timing errors in long sounds
if (stereo == 'right') audio$sound = audio$right
# DC offset
if (killDC) {
audio$sound = killDC(audio$sound, windowLength = windowDC,
samplingRate = audio$samplingRate)
if (!is.null(audio$right) & stereo %in% c('average', 'both'))
audio$right = killDC(audio$right, windowLength = windowDC,
samplingRate = audio$samplingRate)
}
# calculate RMS per frame
myseq = seq(1, max(1, (audio$ls - windowLength_points)), step_points)
r = apply(as.matrix(myseq), 1, function(x) {
sqrt(mean(audio$sound[x:(windowLength_points + x - 1)] ^ 2))
})
names(r) = myseq / audio$samplingRate * 1000 + windowLength / 2 + audio$timeShift * 1000
if (normalize) r = r / audio$scale
# same for the right channel
if (!is.null(audio$right) & stereo %in% c('average', 'both')) {
r2 = apply(as.matrix(myseq), 1, function(x) {
sqrt(mean(audio$right[x:(windowLength_points + x - 1)] ^ 2))
})
names(r2) = names(r)
if (normalize) r2 = r2 / audio$scale
}
if (stereo == 'average') r = (r + r2) / 2
if (stereo == 'both') r = list(left = r, right = r2)
# plotting
if (is.character(audio$savePlots)) {
plot = TRUE
png(filename = paste0(audio$savePlots, audio$filename_noExt, "_rms.png"),
width = width, height = height, units = units, res = res)
}
if (plot) {
if (is.null(main)) {
if (audio$filename_noExt == 'sound') {
main = ''
} else {
main = audio$filename_noExt
}
}
if (stereo != 'both') {
.osc(audio)
points(as.numeric(names(r)), r * audio$scale, type = type,
col = col, lwd = lwd, ...)
} else if (stereo == 'both' & !is.null(audio$right)) {
time = ((1:audio$ls) / audio$samplingRate + audio$timeShift) * 1000
op = par(c('mar', 'mfrow')) # save user's original pars
layout(matrix(c(2, 1), nrow = 2, byrow = TRUE))
par(mfrow = c(2, 1), mar = c(0, par()$mar[2:4]))
plot(time, audio$sound, type = 'n', main = main, xlab = xlab, ylab = ylab,
xaxt = 'n', ylim = c(-audio$scale, audio$scale), ...)
time_location = axTicks(1)
time_labels = convert_sec_to_hms(time_location / 1000, 3)
# axis(side = 1, at = time_location, labels = time_labels)
points(time, audio$sound, type = 'l')
points(as.numeric(names(r$left)), r$left * audio$scale, type = type,
col = col, lwd = lwd, ...)
text(0, audio$scale, adj = c(1, 1), labels = 'L', col = 'blue', cex = 1.5)
par(mar = c(op$mar[1:2], 0, op$mar[4]))
plot(time, audio$right, type = 'n', main = main, xlab = xlab, ylab = ylab,
xaxt = 'n', ylim = c(-audio$scale, audio$scale), ...)
time_location = axTicks(1)
time_labels = convert_sec_to_hms(time_location / 1000, 3)
axis(side = 1, at = time_location, labels = time_labels)
points(time, audio$right, type = 'l')
points(as.numeric(names(r$right)), r$right * audio$scale, type = type,
col = col, lwd = lwd, ...)
text(0, audio$scale, adj = c(1, 1), labels = 'R', col = 'blue', cex = 1.5)
# restore original pars
par('mar' = op$mar, 'mfrow' = op$mfrow)
}
if (is.character(audio$savePlots)) dev.off()
}
return(r)
}
#' Normalize folder
#'
#' Normalizes the amplitude of all wav/mp3 files in a folder based on their peak
#' or RMS amplitude or subjective loudness. This is good for playback
#' experiments, which require that all sounds should have similar intensity or
#' loudness.
#'
#' Algorithm: first all files are rescaled to have the same peak amplitude of
#' \code{maxAmp} dB. If \code{type = 'peak'}, the process ends here. If
#' \code{type = 'rms'}, there are two additional steps. First the original RMS
#' amplitude of all files is calculated per frame by \code{\link{getRMS}}. The
#' "quietest" sound with the lowest summary RMS value is not modified, so its
#' peak amplitude remains \code{maxAmp} dB. All the remaining sounds are
#' rescaled linearly, so that their summary RMS values becomes the same as that
#' of the "quietest" sound, and their peak amplitudes become smaller,
#' \code{<maxAmp}. Finally, if \code{type = 'loudness'}, the subjective
#' loudness of each sound is estimated by \code{\link{getLoudness}}, which
#' assumes frequency sensitivity typical of human hearing. The following
#' normalization procedure is similar to that for \code{type = 'rms'}.
#'
#' @seealso \code{\link{getRMS}} \code{\link{analyze}} \code{\link{getLoudness}}
#'
#' @inheritParams getRMS
#' @param myfolder full path to folder containing input audio files
#' @param type normalize so the output files has the same peak amplitude
#' ('peak'), root mean square amplitude ('rms'), or subjective loudness in
#' sone ('loudness')
#' @param maxAmp maximum amplitude in dB (0 = max possible, -10 = 10 dB below
#' max possible, etc.)
#' @param summaryFun should the output files have the same mean / median / max
#' etc rms amplitude or loudness? (summaryFun has no effect if type = 'peak')
#' @param saveAudio full path to where the normalized files should be saved
#' (defaults to 'myfolder/normalized')
#' @export
#' @examples
#' \dontrun{
#' # put a few short audio files in a folder, eg '~/Downloads/temp'
#' getRMS('~/Downloads/temp2', summaryFun = 'mean')$summary # different
#' normalizeFolder('~/Downloads/temp2', type = 'rms', summaryFun = 'mean',
#' saveAudio = '~/Downloads/temp2/normalized')
#' getRMS('~/Downloads/temp2/normalized', summaryFun = 'mean')$summary # same
#' # If the saved audio files are treated as stereo with one channel missing,
#' # try reconverting with ffmpeg (saving is handled by tuneR::writeWave)
#' }
normalizeFolder = function(
myfolder,
type = c('peak', 'rms', 'loudness')[1],
maxAmp = 0,
summaryFun = 'mean',
windowLength = 50,
step = NULL,
overlap = 70,
killDC = FALSE,
windowDC = 200,
saveAudio = NULL,
reportEvery = NULL
) {
time_start = proc.time() # timing
filenames = list.files(myfolder, pattern = "*.wav|.mp3|.WAV|.MP3",
full.names = TRUE)
filesizes = file.info(filenames)$size
if (length(filenames) < 1) {
stop(paste('No wav/mp3 files found in', myfolder))
}
# in order to provide more accurate estimates of time to completion,
# check the size of all files in the target folder
n = length(filenames)
## load all the files
print('Loading...')
files = vector('list', n)
for (i in 1:n) {
ext = substr(filenames[i], (nchar(filenames[i]) - 2), nchar(filenames[i]))
if (ext %in% c('wav', 'WAV')) {
files[[i]] = tuneR::readWave(filenames[i])
} else if (ext %in% c('mp3', 'MP3')) {
files[[i]] = tuneR::readMP3(filenames[i])
} else {
stop('Input not recognized')
}
}
## process all the files
print('Processing...')
# for either peak or RMS normalization, start by peak normalization to maxAmp dB
level = 10 ^ (maxAmp / 20)
for (i in 1:n) {
files[[i]] = tuneR::normalize(files[[i]],
unit = as.character(files[[i]]@bit),
rescale = TRUE, level = level)
}
# for RMS- or loudness-normalization, perform additional steps
if (type %in% c('rms', 'loudness')) {
perSound = vector('list', n)
if (type == 'rms') {
for (i in 1:n) {
# calculate the RMS amplitude of each file
perSound[[i]] = getRMS(files[[i]],
windowLength = windowLength,
step = step,
overlap = overlap,
scale = 2^(files[[i]]@bit - 1),
killDC = killDC,
windowDC = windowDC,
plot = FALSE)$detailed
}
} else if (type == 'loudness') {
for (i in 1:n) {
# estimate subjective loudness of each file
perSound[[i]] = getLoudness(files[[i]],
scale = 2^(files[[i]]@bit - 1),
windowLength = windowLength,
step = step,
plot = FALSE)$loudness
}
}
# summary measure per file
summaryPerSound = unlist(lapply(perSound, summaryFun))
names(summaryPerSound) = basename(filenames)
# find the quietest file
ref = which.min(summaryPerSound)
# the quietest file is untouched, but all others are rescaled to have the
# same RMS/loudness as the quietest one
for (i in 1:n) {
if (i != ref) {
if (type == 'rms') {
rescale = summaryPerSound[ref] / summaryPerSound[i]
} else if (type == 'loudness') {
rescale = (summaryPerSound[ref] / summaryPerSound[i]) ^ (5 / 3)
}
files[[i]]@left = as.integer(round(files[[i]]@left * rescale))
}
}
}
# save the rescaled files
if (is.null(saveAudio)) saveAudio = paste0(myfolder, '/normalized')
if (!is.na(saveAudio)) {
print('Saving...')
if (!dir.exists(saveAudio)) dir.create(saveAudio)
for (i in 1:n) {
file_wo_ext = sub("([^.]+)\\.[[:alnum:]]+$", "\\1", basename(filenames[i]))
tuneR::writeWave(
files[[i]],
filename = paste0(saveAudio, '/', file_wo_ext, '.wav')
)
}
}
# report time
if (is.null(reportEvery) || is.finite(reportEvery)) {
reportTime(i = n, nIter = n, reportEvery = reportEvery,
time_start = time_start, jobs = filesizes)
}
}
#' Flat envelope / compressor
#'
#' Applies a compressor - that is, flattens the amplitude envelope of a
#' waveform, reducing the difference in amplitude between loud and quiet
#' sections. This is achieved by dividing the waveform by some function of its
#' smoothed amplitude envelope (Hilbert, peak or root mean square).
#'
#' @inheritParams spectrogram
#' @inheritParams segment
#' @param compression the amount of compression to apply: 0 = none, 1 = maximum
#' @param method hil = Hilbert envelope, rms = root mean square amplitude, peak
#' = peak amplitude per window
#' @param windowLength the length of smoothing window, ms
#' @param windowLength_points the length of smoothing window, points. If
#' specified, overrides \code{windowLength}
#' @param killDC if TRUE, dynamically removes DC offset or similar deviations of
#' average waveform from zero (see examples)
#' @param dynamicRange parts of sound quieter than \code{-dynamicRange} dB will
#' not be amplified
#' @param plot if TRUE, plots the original sound, the smoothed envelope, and
#' the compressed sound
#' @param saveAudio full path to the folder in which to save the compressed
#' sound(s)
#' @param col the color of amplitude contours
#' @param ... other graphical parameters passed to \code{points()} that control
#' the appearance of amplitude contours, eg \code{lwd, lty}, etc.
#'
#' @return If the input is a single audio (file, Wave, or numeric vector),
#' returns the compressed waveform as a numeric vector with the original
#' sampling rate and scale. If the input is a folder with several audio files,
#' returns a list of compressed waveforms, one for each file.
#' @export
#' @examples
#' a = rnorm(500) * seq(1, 0, length.out = 500)
#' b = flatEnv(a, 1000, plot = TRUE, windowLength_points = 5) # too short
#' c = flatEnv(a, 1000, plot = TRUE, windowLength_points = 450) # too long
#' d = flatEnv(a, 1000, plot = TRUE, windowLength_points = 100) # about right
#'
#' \dontrun{
#' s = soundgen(sylLen = 1000, ampl = c(0, -40, 0), plot = TRUE)
#' # playme(s)
#' s_flat1 = flatEnv(s, 16000, dynamicRange = 60, plot = TRUE,
#' windowLength = 50, method = 'hil')
#' s_flat2 = flatEnv(s, 16000, dynamicRange = 60, plot = TRUE,
#' windowLength = 10, method = 'rms')
#' s_flat3 = flatEnv(s, 16000, dynamicRange = 60, plot = TRUE,
#' windowLength = 10, method = 'peak')
#' # playme(s_flat2)
#'
#' # Remove DC offset
#' s1 = c(rep(0, 50), runif(1000, -1, 1), rep(0, 50)) +
#' seq(.3, 1, length.out = 1100)
#' s2 = flatEnv(s1, 16000, plot = TRUE, windowLength_points = 50, killDC = FALSE)
#' s3 = flatEnv(s1, 16000, plot = TRUE, windowLength_points = 50, killDC = TRUE)
#'
#' # Compress and save all audio files in a folder
#' s4 = flatEnv('~/Downloads/temp2',
#' method = 'peak', compression = .5,
#' saveAudio = '~/Downloads/temp2/compressed',
#' savePlots = '~/Downloads/temp2/compressed',
#' col = 'green', lwd = 5)
#' osc(s4[[1]])
#' }
flatEnv = function(x,
samplingRate = NULL,
scale = NULL,
compression = 1,
method = c('hil', 'rms', 'peak')[1],
windowLength = 50,
windowLength_points = NULL,
killDC = FALSE,
dynamicRange = 40,
reportEvery = NULL,
cores = 1,
saveAudio = NULL,
plot = FALSE,
savePlots = NULL,
col = 'blue',
width = 900,
height = 500,
units = 'px',
res = NA,
...) {
# match args
myPars = c(as.list(environment()), list(...))
# exclude some args
myPars = myPars[!names(myPars) %in% c(
'x', 'samplingRate', 'scale', 'reportEvery', 'cores', 'savePlots', 'saveAudio')]
pa = processAudio(x = x,
samplingRate = samplingRate,
scale = scale,
saveAudio = saveAudio,
savePlots = savePlots,
funToCall = '.flatEnv',
myPars = myPars,
reportEvery = reportEvery,
cores = cores)
# htmlPlots
if (!is.null(pa$input$savePlots) && pa$input$n > 1) {
try(htmlPlots(pa$input, savePlots = savePlots, changesAudio = TRUE,
suffix = "compressor", width = paste0(width, units)))
}
# prepare output
if (pa$input$n == 1) {
result = pa$result[[1]]
} else {
result = pa$result
}
invisible(result)
}
#' @rdname flatEnv
#' @export
compressor = flatEnv
#' Flat envelope per sound
#'
#' Internal soundgen function
#'
#' @param audio a list returned by \code{readAudio}
#' @inheritParams flatEnv
#' @keywords internal
.flatEnv = function(audio,
compression = 1,
method = c('hil', 'rms', 'peak')[1],
windowLength = 50,
windowLength_points = NULL,
killDC = FALSE,
dynamicRange = 40,
plot = FALSE,
col = 'blue',
width = 900,
height = 500,
units = 'px',
res = NA,
...) {
if (!is.numeric(windowLength_points)) {
if (is.numeric(windowLength)) {
if (is.numeric(audio$samplingRate)) {
windowLength_points = windowLength / 1000 * audio$samplingRate
} else {
stop(paste(
'Please specify either windowLength (ms) plus samplingRate (Hz)',
'or the length of smoothing window in points (windowLength_points)'))
}
}
}
# audio$scale = original scale (eg -1 to +1 gives m = 1)
throwaway_lin = 10 ^ (-dynamicRange / 20) * audio$scale
# from dB to linear + normalize
# remove DC offset
if (killDC) {
soundFlat = killDC(sound = audio$sound,
windowLength_points = windowLength_points,
plot = FALSE)
} else {
soundFlat = audio$sound
}
# get smoothed amplitude envelope
env = getEnv(sound = soundFlat,
windowLength_points = windowLength_points,
method = method)
env[env < 0] = 0
# don't amplify very quiet sections
idx = which(env > throwaway_lin)
# flatten amplitude envelope
soundFlat[idx] = soundFlat[idx] * (1 - compression) +
soundFlat[idx] / env[idx] * audio$scale * compression
# re-normalize to original scale
soundFlat = soundFlat / max(abs(soundFlat))
if (!is.null(audio$scale_used))
soundFlat = soundFlat * audio$scale_used
# PLOTTING
if (is.character(audio$savePlots)) {
plot = TRUE
png(filename = paste0(audio$savePlots, audio$filename_noExt, "_compressor.png"),
width = width, height = height, units = units, res = res)
}
if (plot) {
ylim = range(c(audio$sound, soundFlat))
op = par('mfrow')
par(mfrow = c(1, 2))
plot(audio$sound, type = 'l', main = 'Original', ylim = ylim)
points(env, type = 'l', col = col, ...)
env_new = getEnv(sound = soundFlat,
windowLength_points = windowLength_points,
method = method)
plot(soundFlat, type = 'l', main = 'Compressed', ylim = ylim)
points(env_new, type = 'l', col = col, ...)
par(mfrow = op)
if (is.character(audio$savePlots)) dev.off()
}
if (!is.null(audio$saveAudio)) {
if (!dir.exists(audio$saveAudio)) dir.create(audio$saveAudio)
filename = paste0(audio$saveAudio, '/', audio$filename_noExt, '.wav')
writeAudio(soundFlat, audio = audio, filename = filename)
}
return(soundFlat)
}
#' Transplant envelope
#'
#' Extracts a smoothed amplitude envelope of the \code{donor} sound and applies
#' it to the \code{recipient} sound. Both sounds are provided as numeric
#' vectors; they can differ in length and sampling rate. Note that the result
#' depends on the amount of smoothing (controlled by \code{windowLength}) and
#' the chosen method of calculating the envelope. Very similar to
#' \code{\link[seewave]{setenv}}, but with a different smoothing algorithm and
#' with a choice of several types of envelope: hil, rms, or peak.
#'
#' @seealso \code{\link{flatEnv}} \code{\link[seewave]{setenv}}
#'
#' @param donor the sound that "donates" the amplitude envelope
#' @param recipient the sound that needs to have its amplitude envelope adjusted
#' @param samplingRateD,samplingRateR sampling rate of the donor and recipient,
#' respectively (only needed for vectors, not files)
#' @inheritParams flatEnv
#' @return Returns the recipient sound with the donor's amplitude envelope - a
#' numeric vector with the same sampling rate as the recipient
#' @export
#' @examples
#' donor = rnorm(500) * seq(1, 0, length.out = 500)
#' recipient = soundgen(sylLen = 600, addSilence = 50)
#' transplantEnv(donor, samplingRateD = 200,
#' recipient, samplingRateR = 16000,
#' windowLength = 50, method = 'hil', plot = TRUE)
#' transplantEnv(donor, samplingRateD = 200,
#' recipient, samplingRateR = 16000,
#' windowLength = 10, method = 'peak', plot = TRUE)
transplantEnv = function(donor,
samplingRateD = NULL,
recipient,
samplingRateR = samplingRateD,
windowLength = 50,
method = c('hil', 'rms', 'peak')[3],
killDC = FALSE,
dynamicRange = 80,
plot = FALSE) {
# Read inputs
donor = readAudio(
donor,
input = checkInputType(donor),
samplingRate = samplingRateD
)
recipient = readAudio(
recipient,
input = checkInputType(recipient),
samplingRate = samplingRateR
)
windowLength_points_donor = windowLength / 1000 * donor$samplingRate
windowLength_points_recip = windowLength / 1000 * recipient$samplingRate
throwaway_lin = 10 ^ (-dynamicRange / 20) * recipient$scale
# get the amplitude envelope of the recipient
env_recipient = getEnv(sound = recipient$sound,
windowLength_points = windowLength_points_recip,
method = method)
len_recipient = length(env_recipient)
# don't amplify very quiet sections
env_recip_cut = env_recipient
env_recip_cut[env_recip_cut < throwaway_lin] = throwaway_lin
# plot(env_recip_cut, type = 'l')
# get the amplitude envelope of the donor
env_donor = getEnv(sound = donor$sound,
windowLength_points = windowLength_points_donor,
method = method)
env_donor1 = env_donor / max(env_donor) # normalize
env_donor1 = approx(env_donor1, n = len_recipient)$y
# plot(env_donor1, type = 'l')
# flatten the envelope of the recipient and apply the donor's envelope
out = recipient$sound / env_recip_cut * env_donor1
out = out / max(abs(out)) * recipient$scale
# plot(out, type = 'l')
if (plot) {
len_donor = length(env_donor)
max_donor = max(abs(donor$sound))
max_recipient = max(c(abs(recipient$sound), abs(out)))
op = par('mfrow')
par(mfrow = c(1, 3))
.osc(donor, main = 'Donor', ylim = c(-max_donor, max_donor),
xlab = '', ylab = 'Amplitude', midline = FALSE)
par(new = TRUE)
.osc(list(sound = env_donor,
samplingRate = donor$samplingRate,
ls = len_donor,
filename_noExt = ''),
lty = 1, col = 'blue', xlab = '', ylab = '', midline = FALSE,
xaxt = 'n', yaxt = 'n', ylim = c(-max_donor, max_donor))
.osc(recipient, main = 'Recipient', ylim = c(-max_recipient, max_recipient),
xlab = 'Time, s', ylab = '', midline = FALSE)
par(new = TRUE)
.osc(list(sound = env_recipient,
samplingRate = recipient$samplingRate,
ls = len_recipient,
filename_noExt = ''),
lty = 1, col = 'blue', xlab = '', ylab = '', midline = FALSE,
xaxt = 'n', yaxt = 'n', ylim = c(-max_recipient, max_recipient))
.osc(list(sound = out,
samplingRate = recipient$samplingRate,
ls = len_recipient,
filename_noExt = ''),
main = 'Output', ylim = c(-max_recipient, max_recipient),
xlab = '', ylab = '', midline = FALSE)
par(new = TRUE)
.osc(list(
sound = getEnv(out, windowLength_points_recip, method),
samplingRate = recipient$samplingRate,
ls = len_recipient,
filename_noExt = ''),
lty = 1, col = 'blue', xlab = '', ylab = '', midline = FALSE,
xaxt = 'n', yaxt = 'n', ylim = c(-max_recipient, max_recipient))
par(mfrow = op)
}
invisible(out)
}
#' Add amplitude modulation
#'
#' Adds sinusoidal or logistic amplitude modulation to a sound. This produces
#' additional harmonics in the spectrum at ±am_freq around each original
#' harmonic and makes the sound rough. The optimal frequency for creating a
#' perception of roughness is ~70 Hz (Fastl & Zwicker "Psychoacoustics").
#' Sinusoidal AM creates a single pair of new harmonics, while non-sinusoidal AM
#' creates more extra harmonics (see examples).
#' @inheritParams spectrogram
#' @inheritParams soundgen
#' @param play if TRUE, plays the processed audio
#' @param saveAudio full (!) path to folder for saving the processed audio; NULL
#' = don't save, '' = same as input folder (NB: overwrites the originals!)
#' @param plot if TRUE, plots the amplitude modulation
#' @export
#' @examples
#' sound1 = soundgen(pitch = c(200, 300), addSilence = 0)
#' s1 = addAM(sound1, 16000, amDep = c(0, 50, 0), amFreq = 75, plot = TRUE)
#' # playme(s1)
#' \dontrun{
#' # Parameters can be specified as in the soundgen() function, eg:
#' s2 = addAM(sound1, 16000,
#' amDep = list(time = c(0, 50, 52, 200, 201, 300),
#' value = c(0, 0, 35, 25, 0, 0)),
#' plot = TRUE, play = TRUE)
#'
#' # Sinusoidal AM produces exactly 2 extra harmonics at ±am_freq
#' # around each f0 harmonic:
#' s3 = addAM(sound1, 16000, amDep = 30, amFreq = c(50, 80),
#' amType = 'sine', plot = TRUE, play = TRUE)
#' spectrogram(s3, 16000, windowLength = 150, ylim = c(0, 2))
#'
#' # Non-sinusoidal AM produces multiple new harmonics,
#' # which can resemble subharmonics...
#' s4 = addAM(sound1, 16000, amDep = 70, amFreq = 50, amShape = -1,
#' plot = TRUE, play = TRUE)
#' spectrogram(s4, 16000, windowLength = 150, ylim = c(0, 2))
#'
#' # ...but more often look like sidebands
#' sound3 = soundgen(sylLen = 600, pitch = c(800, 1300, 1100), addSilence = 0)
#' s5 = addAM(sound3, 16000, amDep = c(0, 30, 100, 40, 0),
#' amFreq = 105, amShape = -.3,
#' plot = TRUE, play = TRUE)
#' spectrogram(s5, 16000, ylim = c(0, 5))
#'
#' # Feel free to add AM stochastically:
#' s6 = addAM(sound1, 16000,
#' amDep = rnorm(10, 40, 20), amFreq = rnorm(20, 70, 20),
#' plot = TRUE, play = TRUE)
#' spectrogram(s6, 16000, windowLength = 150, ylim = c(0, 2))
#'
#' # If am_freq is locked to an integer ratio of f0, we can get subharmonics
#' # For ex., here is with pitch 400-600-400 Hz (soundgen interpolates pitch
#' # on a log scale and am_freq on a linear scale, so we align them by extracting
#' # a long contour on a log scale for both)
#' con = getSmoothContour(anchors = c(400, 600, 400),
#' len = 20, thisIsPitch = TRUE)
#' s = soundgen(sylLen = 1500, pitch = con, amFreq = con/3, amDep = 30,
#' plot = TRUE, play = TRUE, ylim = c(0, 3))
#'
#' # Process all files in a folder and save the modified audio
#' addAM('~/Downloads/temp', saveAudio = '~/Downloads/temp/AM',
#' amFreq = 70, amDep = c(0, 50))
#' }
addAM = function(x,
samplingRate = NULL,
amDep = 25,
amFreq = 30,
amType = c('logistic', 'sine')[1],
amShape = 0,
invalidArgAction = c('adjust', 'abort', 'ignore')[1],
plot = FALSE,
play = FALSE,
saveAudio = NULL,
reportEvery = NULL,
cores = 1) {
# check the format of AM pars
anchors = c('amDep', 'amFreq', 'amShape')
for (anchor in anchors) {
assign(anchor, reformatAnchors(get(anchor)))
}
rm('anchor', 'anchors')
# match args
myPars = c(as.list(environment()))
# exclude some args
myPars = myPars[!names(myPars) %in% c(
'x', 'samplingRate', 'reportEvery', 'cores', 'saveAudio')]
pa = processAudio(x,
samplingRate = samplingRate,
saveAudio = saveAudio,
funToCall = '.addAM',
myPars = myPars,
reportEvery = reportEvery,
cores = cores)
# prepare output
if (pa$input$n == 1) {
result = pa$result[[1]]
} else {
result = pa$result
}
invisible(result)
}
#' Add AM to a sound
#'
#' Internal soundgen function, see \code{\link{addAM}}.
#'
#' @inheritParams addAM
#' @param audio a list returned by \code{readAudio}
#' @keywords internal
.addAM = function(
audio,
amDep = 25,
amFreq = 30,
amType = c('logistic', 'sine')[1],
amShape = 0,
invalidArgAction = c('adjust', 'abort', 'ignore')[1],
plot = FALSE,
play = FALSE
) {
# vectorize
amPar_vect = c('amDep', 'amFreq', 'amShape')
# just to get rid of of NOTE on CRAN:
amDep_vector = amFreq_vector = amShape_vector = vector()
for (p in amPar_vect) {
p_unique_value = unique(get(p)$value)
if (length(p_unique_value) > 1) {
if (invalidArgAction == 'ignore') {
valueFloor_p = valueCeiling_p = NULL
} else {
valueFloor_p = permittedValues[p, 'low']
valueCeiling_p = permittedValues[p, 'high']
}
p_vectorized = getSmoothContour(
anchors = get(p),
len = audio$ls,
interpol = 'approx',
valueFloor = valueFloor_p,
valueCeiling = valueCeiling_p
)
# plot(p_vectorized, type = 'l')
assign(paste0(p, '_vector'), p_vectorized)
} else {
assign(paste0(p, '_vector'), p_unique_value)
}
}
# prepare am vector
if (amType == 'sine') {
if (length(amFreq_vector) == 1) {
int = amFreq_vector * (1:audio$ls)
} else {
int = cumsum(amFreq_vector)
}
sig = .5 + .5 * cos(2 * pi * int / audio$samplingRate)
} else {
sig = getSigmoid(len = audio$ls,
samplingRate = audio$samplingRate,
freq = amFreq_vector,
shape = amShape_vector)
}
# plot(sig, type = 'l')
# sig is on a scale [0, 1]
am = 1 - sig * amDep_vector / 100
sound_am = audio$sound * am
if (plot) {
.osc(list(sound = am,
samplingRate = audio$samplingRate,
ls = length(am)),
main = 'Amplitude modulation',
xlab = 'Time, ms',
ylab = '',
ylim = c(0, 1),
midline = FALSE)
}
if (play) playme(sound_am, audio$samplingRate)
if (is.character(audio$saveAudio)) {
filename = paste0(audio$saveAudio, '/', audio$filename_noExt, '.wav')
writeAudio(sound_am, audio = audio, filename = filename)
}
invisible(sound_am)
}
#' Get amplitude envelope
#'
#' Returns the smoothed amplitude envelope of a waveform on the original scale.
#' Unlike seewave::env, this function always returns an envelope of the same
#' length as the original sound, regardless of the amount of smoothing.
#' @param sound numeric vector
#' @param windowLength_points the length of smoothing window, in points
#' @param method 'peak' for peak amplitude per window, 'rms' for root mean
#' square amplitude, 'mean' for mean (for DC offset removal), 'hil' for
#' Hilbert, 'raw' for low-pass filtering the actual sound
#' @export
#' @examples
#' a = rnorm(500) * seq(1, 0, length.out = 500)
#' windowLength_points = 50
#' scale = max(abs(a))
#' plot(a, type = 'l', ylim = c(-scale, scale))
#' points(soundgen:::getEnv(a, windowLength_points, 'rms'),
#' type = 'l', col = 'red')
#' points(soundgen:::getEnv(a, windowLength_points, 'peak'),
#' type = 'l', col = 'green')
#' points(soundgen:::getEnv(a, windowLength_points, 'hil'),
#' type = 'l', col = 'blue')
#' points(soundgen:::getEnv(a, windowLength_points, 'mean'),
#' type = 'l', lty = 3, lwd = 3)
getEnv = function(
sound,
windowLength_points,
method = c('rms', 'hil', 'peak', 'raw', 'mean')[1]
) {
if (!method %in% c('rms', 'hil', 'peak', 'raw', 'mean'))
stop("Valid values for method are c('rms', 'hil', 'peak', 'mean')")
windowLength_points = round(windowLength_points)
sound = c(rep(0, windowLength_points),
sound,
rep(0, windowLength_points))
len = length(sound)
if (method == 'peak') sound_abs = abs(sound) # avoid repeated calculations
# moving window operations: get per window, then upsample
if (windowLength_points >= len / 2) {
# short sound relative to window - just take beginning and end (2 points)
s = c(1, len)
} else {
s = c(1,
seq(from = floor(windowLength_points / 2),
to = length(sound) - floor(windowLength_points / 2),
by = windowLength_points),
length(sound))
}
# s is a sequence of starting indices for windows over which we average
envShort = rep(NA, length(s) - 1)
for (i in 1:(length(s) - 1)) {
seg = s[i] : s[i+1]
if (method == 'peak') {
# get moving peak amplitude
envShort[i] = max(sound_abs[seg])
} else if (method == 'mean' || method == 'raw') {
envShort[i] = mean(sound[seg])
} else if (method == 'rms') {
envShort[i] = mean(sqrt(sound[seg] ^ 2))
} else if (method == 'hil') {
envShort[i] = mean(Mod(seewave::hilbert(sound[seg],
f = 1, # not actually needed
fftw = FALSE)))
}
}
# upsample and smooth
env = getSmoothContour(
anchors = data.frame(time = seq(0, 1, length.out = length(envShort)),
value = envShort),
len = length(sound),
valueFloor = 0,
loessSpan = 1
)
# remove the zero-padded regions
env = env[(windowLength_points + 1):(len - windowLength_points)]
# osc(env)
return(env)
}
#' Kill DC
#'
#' Removes DC offset or similar disbalance in a waveform dynamically, by
#' subtracting a smoothed ~moving average. Simplified compared to a true moving
#' average, but very fast (a few ms per second of 44100 audio).
#' @inheritParams flatEnv
#' @param plot if TRUE, plots the original sound, smoothed moving average, and
#' modified sound
#' @keywords internal
#' @examples
#' # remove static DC offset
#' a = rnorm(500) + .3
#' b = soundgen:::killDC(a, windowLength_points = 500, plot = TRUE)
#'
#' # remove trend
#' a = rnorm(500) + seq(0, 1, length.out = 500)
#' b = soundgen:::killDC(a, windowLength_points = 100, plot = TRUE)
#'
#' # can also be used as a high-pass filter
#' a = rnorm(500) + sin(1:500 / 50)
#' b = soundgen:::killDC(a, windowLength_points = 25, plot = TRUE)
killDC = function(sound,
windowLength = 200,
samplingRate = 16000,
windowLength_points = NULL,
plot = FALSE) {
if (!is.numeric(windowLength_points)) {
if (is.numeric(windowLength)) {
if (is.numeric(samplingRate)) {
windowLength_points = windowLength / 1000 * samplingRate
} else {
stop(paste('Please specify either windowLength (ms) plus samplingRate (Hz)',
'or the length of smoothing window in points (windowLength_points)'))
}
}
}
env = getEnv(sound = sound,
windowLength_points = windowLength_points,
method = 'mean')
soundNorm = sound - env
if (plot) {
op = par('mfrow')
par(mfrow = c(1, 2))
plot(sound, type = 'l', main = 'Original')
points(env, type = 'l', lty = 1, col = 'blue')
points(rep(0, length(sound)), type = 'l', lty = 2)
plot(soundNorm, type = 'l', main = 'Env removed')
points(rep(0, length(sound)), type = 'l', col = 'blue')
par(mfrow = op)
}
return(soundNorm)
}
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