analyzeFolder: Analyze sound
In tatters/soundgen_beta: Parametric Voice Synthesis

Description Usage Arguments Value Examples

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))

`myfolder`	full path to target folder
`verbose`	if TRUE, reports progress and estimated time left
`samplingRate`	sampling rate of `x` (only needed if `x` is a numeric vector, rather than a .wav file)
`silence`	(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.
`windowLength`	length of FFT window, ms
`step`	you can override `overlap` by specifying FFT step, ms
`overlap`	overlap between successive FFT frames, %
`wn`	window type: gaussian, hanning, hamming, bartlett, rectangular, blackman, flattop
`zp`	window length after zero padding, points
`cutFreq`	(>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
`nFormants`	the number of formants to extract per FFT frame. Calls `findformants` with default settings
`pitchMethods`	methods of pitch estimation to consider for determining pitch contour: 'autocor' = autocorrelation (~PRAAT), 'cep' = cepstral, 'spec' = spectral (~BaNa), 'dom' = lowest dominant frequency band
`entropyThres`	pitch tracking is not performed for frames with Weiner entropy above `entropyThres`, but other spectral descriptives are still calculated
`pitchFloor`	absolute bounds for pitch candidates (Hz)
`pitchCeiling`	absolute bounds for pitch candidates (Hz)
`priorMean`	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)
`priorSD`	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)
`priorPlot`	if TRUE, produces a separate plot of the prior
`nCands`	maximum number of pitch candidates per method (except for `dom`, which returns at most one candidate per frame), normally 1...4
`minVoicedCands`	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)
`domThres`	(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
`domSmooth`	the width of smoothing interval (Hz) for finding `dom`
`autocorThres`	(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.
`autocorSmooth`	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
`cepThres`	(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.
`cepSmooth`	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
`cepZp`	zero-padding of the spectrum used for cepstral pitch detection (final length of spectrum after zero-padding in points, e.g. 2 ^ 13)
`specThres`	(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.
`specPeak`	when looking for putative harmonics in the spectrum, the threshold for peak detection is calculated as `specPeak * (1 - HNR * specHNRslope)`
`specSinglePeakCert`	(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`
`specHNRslope`	when looking for putative harmonics in the spectrum, the threshold for peak detection is calculated as `specPeak * (1 - HNR * specHNRslope)`
`specSmooth`	the width of window for detecting peaks in the spectrum, Hz
`specMerge`	pitch candidates within `specMerge` semitones are merged with boosted certainty
`shortestSyl`	the smallest length of a voiced segment (ms) that constitutes a voiced syllable (shorter segments will be replaced by NA, as if unvoiced)
`shortestPause`	the smallest gap between voiced syllables (ms) that means they shouldn't be merged into one voiced syllable
`interpolWin`	control the behavior of interpolation algorithm when postprocessing pitch candidates. To turn off interpolation, set `interpolWin` to NULL. See `soundgen:::pathfinder` for details.
`interpolTol`	control the behavior of interpolation algorithm when postprocessing pitch candidates. To turn off interpolation, set `interpolWin` to NULL. See `soundgen:::pathfinder` for details.
`interpolCert`	control the behavior of interpolation algorithm when postprocessing pitch candidates. To turn off interpolation, set `interpolWin` to NULL. See `soundgen:::pathfinder` for details.
`pathfinding`	method of finding the optimal path through pitch candidates: 'none' = best candidate per frame, 'fast' = simple heuristic, 'slow' = annealing. See `soundgen:::pathfinder`
`annealPars`	a list of control parameters for postprocessing of pitch contour with SANN algorithm of `optim`. This is only relevant if `pathfinding = 'slow'`
`certWeight`	(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
`snakeStep`	optimized path through pitch candidates is further processed to minimize the elastic force acting on pitch contour. To disable, set `snakeStep` to NULL
`snakePlot`	if TRUE, plots the snake
`smooth`	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
`smoothVars`	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
`summary`	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
`plot`	if TRUE, produces a spectrogram with pitch contour overlaid
`savePath`	if a valid path is specified, a plot is saved in this folder (defaults to NA)
`specPlot`	a list of graphical parameters passed to `spectrogram`. Set to `NULL` to suppress plotting just the spectrogram
`pitchPlot`	a list of graphical parameters for displaying the final pitch contour. Set to `NULL` or `NA` to suppress
`candPlot`	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')

## End(Not run)