Acoustic analysis of all .wav files in a folder.
analyzeFolder(myfolder, verbose = TRUE, samplingRate = NULL,
silence = 0.04, windowLength = 50, step = NULL, overlap = 50,
wn = "gaussian", zp = 0, cutFreq = 6000, nFormants = 3,
pitchMethods = c("autocor", "spec", "dom"), entropyThres = 0.6,
pitchFloor = 75, pitchCeiling = 3500, priorMean = HzToSemitones(300),
priorSD = 6, priorPlot = FALSE, nCands = 1, minVoicedCands = "autom",
domThres = 0.1, domSmooth = 220, autocorThres = 0.7,
autocorSmooth = NULL, cepThres = 0.3, cepSmooth = NULL, cepZp = 0,
specThres = 0.3, specPeak = 0.35, specSinglePeakCert = 0.4,
specHNRslope = 0.8, specSmooth = 150, specMerge = 1, shortestSyl = 20,
shortestPause = 60, interpolWin = 3, interpolTol = 0.3,
interpolCert = 0.3, pathfinding = c("none", "fast", "slow")[2],
annealPars = list(maxit = 5000, temp = 1000), certWeight = 0.5,
snakeStep = 0.05, snakePlot = FALSE, smooth = 1,
smoothVars = c("pitch", "dom"), summary = TRUE, plot = FALSE,
savePath = NA, specPlot = list(contrast = 0.2, brightness = 0, ylim = c(0,
5)), pitchPlot = list(col = rgb(0, 0, 1, 0.75), lwd = 3),
candPlot = list(levels = c("autocor", "spec", "dom", "cep"), col =
c("green", "red", "orange", "violet"), pch = c(16, 2, 3, 7), cex = 2))full path to target folder
if TRUE, reports progress and estimated time left
sampling rate of x (only needed if
x is a numeric vector, rather than a .wav file)
(0 to 1) frames with mean abs amplitude below silence threshold are not analyzed at all. NB: this number is dynamically updated: the actual silence threshold may be higher depending on the quietest frame, but it will never be lower than this specified number.
length of FFT window, ms
you can override overlap by specifying FFT step, ms
overlap between successive FFT frames, %
window type: gaussian, hanning, hamming, bartlett, rectangular, blackman, flattop
window length after zero padding, points
(>0 to Nyquist, Hz) repeat the calculation of spectral
descriptives after discarding all info above cutFreq.
Recommended if the original sampling rate varies across different analyzed
audio files
the number of formants to extract per FFT frame. Calls
findformants with default settings
methods of pitch estimation to consider for determining pitch contour: 'autocor' = autocorrelation (~PRAAT), 'cep' = cepstral, 'spec' = spectral (~BaNa), 'dom' = lowest dominant frequency band
pitch tracking is not performed for frames with Weiner
entropy above entropyThres, but other spectral descriptives are
still calculated
absolute bounds for pitch candidates (Hz)
absolute bounds for pitch candidates (Hz)
specifies the mean and sd of gamma distribution
describing our prior knowledge about the most likely pitch values for this
file. Specified in semitones: priorMean = HzToSemitones(300),
priorSD = 6 gives a prior with mean = 300 Hz and SD of 6 semitones (half
an octave)
specifies the mean and sd of gamma distribution
describing our prior knowledge about the most likely pitch values for this
file. Specified in semitones: priorMean = HzToSemitones(300),
priorSD = 6 gives a prior with mean = 300 Hz and SD of 6 semitones (half
an octave)
if TRUE, produces a separate plot of the prior
maximum number of pitch candidates per method (except for
dom, which returns at most one candidate per frame), normally 1...4
minimum number of pitch candidates that
have to be defined to consider a frame voiced (defaults to 2 if dom
is among other candidates and 1 otherwise)
(0 to 1) to find the lowest dominant frequency band, we do short-term FFT and take the lowest frequency with amplitude at least domThres
the width of smoothing interval (Hz) for finding
dom
(0 to 1) separate voicing thresholds for detecting pitch candidates with three different methods: autocorrelation, cepstrum, and BaNa algorithm (see Details). Note that HNR is calculated even for unvoiced frames.
the width of smoothing interval (in bins) for finding peaks in the autocorrelation function. Defaults to 7 for sampling rate 44100 and smaller odd numbers for lower values of sampling rate
(0 to 1) separate voicing thresholds for detecting pitch candidates with three different methods: autocorrelation, cepstrum, and BaNa algorithm (see Details). Note that HNR is calculated even for unvoiced frames.
the width of smoothing interval (in bins) for finding peaks in the cepstrum. Defaults to 31 for sampling rate 44100 and smaller odd numbers for lower values of sampling rate
zero-padding of the spectrum used for cepstral pitch detection (final length of spectrum after zero-padding in points, e.g. 2 ^ 13)
(0 to 1) separate voicing thresholds for detecting pitch candidates with three different methods: autocorrelation, cepstrum, and BaNa algorithm (see Details). Note that HNR is calculated even for unvoiced frames.
when looking for putative harmonics in
the spectrum, the threshold for peak detection is calculated as
specPeak * (1 - HNR * specHNRslope)
(0 to 1) if F0 is calculated based on a single
harmonic ratio (as opposed to several ratios converging on the same
candidate), its certainty is taken to be specSinglePeakCert
when looking for putative harmonics in
the spectrum, the threshold for peak detection is calculated as
specPeak * (1 - HNR * specHNRslope)
the width of window for detecting peaks in the spectrum, Hz
pitch candidates within specMerge semitones are
merged with boosted certainty
the smallest length of a voiced segment (ms) that constitutes a voiced syllable (shorter segments will be replaced by NA, as if unvoiced)
the smallest gap between voiced syllables (ms) that means they shouldn't be merged into one voiced syllable
control the behavior of
interpolation algorithm when postprocessing pitch candidates. To turn off
interpolation, set interpolWin to NULL. See
soundgen:::pathfinder for details.
control the behavior of
interpolation algorithm when postprocessing pitch candidates. To turn off
interpolation, set interpolWin to NULL. See
soundgen:::pathfinder for details.
control the behavior of
interpolation algorithm when postprocessing pitch candidates. To turn off
interpolation, set interpolWin to NULL. See
soundgen:::pathfinder for details.
method of finding the optimal path through pitch
candidates: 'none' = best candidate per frame, 'fast' = simple heuristic,
'slow' = annealing. See soundgen:::pathfinder
a list of control parameters for postprocessing of
pitch contour with SANN algorithm of optim. This is
only relevant if pathfinding = 'slow'
(0 to 1) in pitch postprocessing, specifies how much we prioritize the certainty of pitch candidates vs. pitch jumps / the internal tension of the resulting pitch curve
optimized path through pitch candidates is further
processed to minimize the elastic force acting on pitch contour. To
disable, set snakeStep to NULL
if TRUE, plots the snake
if smooth is a positive number, outliers of
the variables in smoothVars are adjusted with median smoothing.
smooth of 1 corresponds to a window of ~100 ms and tolerated
deviation of ~4 semitones. To disable, set smooth to NULL
if smooth is a positive number, outliers of
the variables in smoothVars are adjusted with median smoothing.
smooth of 1 corresponds to a window of ~100 ms and tolerated
deviation of ~4 semitones. To disable, set smooth to NULL
if TRUE, returns only a summary of the measured acoustic variables (mean, median and SD). If FALSE, returns a list containing frame-by-frame values
if TRUE, produces a spectrogram with pitch contour overlaid
if a valid path is specified, a plot is saved in this folder (defaults to NA)
a list of graphical parameters passed to
spectrogram. Set to NULL to suppress plotting just the
spectrogram
a list of graphical parameters for displaying the
final pitch contour. Set to NULL or NA to suppress
a list of graphical parameters for displaying
individual pitch candidates. Set to NULL or NA to suppress
If summary is TRUE, returns a dataframe with one row per audio
file. If summary is FALSE, returns a list of detailed descriptives.
# NOT RUN {
# download 260 sounds from Anikin & Persson (2017)
# http://cogsci.se/personal/results/
# 01_anikin-persson_2016_naturalistics-non-linguistic-vocalizations/260sounds_wav.zip
# unzip them into a folder, say '~/Downloads/temp'
myfolder = '~/Downloads/temp' # 260 .wav files live here
s = analyzeFolder(myfolder, verbose = TRUE) # ~ 15-30 minutes!
# Check accuracy: import manually verified pitch values (our "key")
key = pitchManual # a vector of 260 floats
trial = s$pitch_median
cor(key, trial, use = 'pairwise.complete.obs')
plot(log(key), log(trial))
abline(a=0, b=1, col='red')
# }
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