analyze: Acoustic analysis

In soundgen: Sound Synthesis and Acoustic Analysis

Acoustic analysis

Description

Acoustic analysis of one or more sounds: pitch tracking, basic spectral characteristics, formants, estimated loudness (see getLoudness), roughness (see modulationSpectrum), novelty (see ssm), etc. The default values of arguments are optimized for human non-linguistic vocalizations. For high-precision work, first extract and manually correct pitch contours with pitch_app, PRAAT, or whatever, and then run analyze(pitchManual = ...) with these manual contours. For more information, see https://cogsci.se/soundgen/acoustic_analysis.html

Usage

analyze(
  x,
  samplingRate = NULL,
  scale = NULL,
  from = NULL,
  to = NULL,
  dynamicRange = 80,
  silence = 0.04,
  windowLength = 50,
  step = NULL,
  overlap = 50,
  wn = "gaussian",
  zp = 0,
  cutFreq = NULL,
  nFormants = 3,
  formants = list(),
  loudness = list(SPL_measured = 70),
  roughness = list(msType = "1D", specMethod = "spectrum", amRes = 1, specFun_pars =
    list(windowLength = 25, step = 2)),
  novelty = list(specFun = "melspec", kernelLen = 1000),
  pitchMethods = c("dom", "autocor"),
  pitchManual = NULL,
  pitchFloor = 75,
  pitchCeiling = 1000,
  priorMean = 300,
  priorSD = 6,
  priorAdapt = TRUE,
  nCands = 1,
  minVoicedCands = NULL,
  pitchDom = list(domThres = 0.1, domSmooth = 220),
  pitchAutocor = list(autocorThres = 0.7, autocorSmooth = 7, autocorUpsample = 25,
    autocorBestPeak = 0.975, interpol = "sinc"),
  pitchCep = list(cepThres = 0.75, cepZp = 0),
  pitchSpec = list(specThres = 0.05, specPeak = 0.25, specHNRslope = 0.8, specSmooth =
    150, specMerge = 0.1, specSinglePeakCert = 0.4, specRatios = 3),
  pitchHps = list(hpsNum = 5, hpsThres = 0.1, hpsNorm = 2, hpsPenalty = 2),
  pitchZc = list(zcThres = 0.1, zcWin = 5),
  harmHeight = list(harmThres = 3, harmTol = 0.25, harmPerSel = 5),
  subh = list(method = c("cep", "pitchCands", "harm")[1], nSubh = 5, tol = 0.05, nHarm =
    5, harmThres = 12, harmTol = 0.25),
  flux = list(thres = 0.15),
  amRange = c(10, 60),
  fmRange = NULL,
  shortestSyl = 20,
  shortestPause = 60,
  interpolPitch = list(win = 75, tol = 0.3, cert = 0.3),
  certWeight = 0.5,
  smooth = 1,
  smoothVars = c("pitch", "dom"),
  summaryFun = c("mean", "median", "sd"),
  invalidArgAction = c("adjust", "abort", "ignore"),
  reportEvery = NULL,
  cores = 1,
  plot = FALSE,
  osc = c("linear", "dB", "none"),
  showLegend = TRUE,
  savePlots = FALSE,
  embed = FALSE,
  pitchPlot = list(col = rgb(0, 0, 1, 0.75), lwd = 3, showPrior = TRUE),
  extraContour = NULL,
  ylim = NULL,
  xlab = "Time",
  ylab = NULL,
  main = NULL,
  width = 900,
  height = 500,
  units = "px",
  res = NA,
  ...
)

Arguments

x	path to a folder, one or more wav or mp3 files c('file1.wav', 'file2.mp3'), Wave object, numeric vector, or a list of Wave objects or numeric vectors
samplingRate	sampling rate of x (only needed if x is a numeric vector)
scale	maximum possible amplitude of input, used to normalize the input vector (only needed if x is a numeric vector)
from, to	if NULL (default), analyzes the whole sound, otherwise from...to (s)
dynamicRange	regions under -dynamicRange dB are treated as silent
silence	(0 to 1 as proportion of max amplitude of the anayzed sound) frames with RMS amplitude below silence * max_ampl adjusted by scale are not analyzed at all
windowLength	length of the analysis window, ms
step	step between successive windows, ms; if provided, overrides overlap; because digital audio is sampled at discrete time intervals of 1/samplingRate, the actual step and thus the time stamps of STFT frames may be slightly different - e.g., 24.98866 instead of 25.0 ms
overlap	overlap between successive windows, %
wn	window type accepted by winFun: character string or function
zp	window length after zero padding, samples. No padding is performed if zp < analysis window length in samples. If NULL, the signal is padded to a good number for speeding up the FFT
cutFreq	if specified, spectral descriptives (peakFreq, specCentroid, specSlope, and quartiles) are calculated only between cutFreq[1] and cutFreq[2], Hz. If a single number is given, analyzes frequencies from 0 to cutFreq. For ex., when analyzing recordings with varying sampling rates, set to half the lowest sampling rate to make the spectra more comparable.
nFormants	the number of formants to extract per STFT frame (0 = no formant analysis, NULL = as many as possible)
formants	a list of arguments passed to findformants for LPC analysis
loudness	a list of parameters passed to getLoudness for measuring subjective loudness, namely SPL_measured, spreadSpectrum, sharpnessMethod. NULL = skip loudness analysis
roughness	a list of parameters passed to modulationSpectrum for measuring roughness and fluctuation strength. NULL = skip roughness analysis
novelty	a list of parameters passed to ssm for measuring spectral novelty. NULL = skip novelty analysis
pitchMethods	methods of pitch estimation to consider for determining pitch contour: 'autocor' = autocorrelation (~PRAAT), 'cep' = cepstral, 'spec' = spectral (~BaNa), 'dom' = lowest dominant frequency band, 'hps' = harmonic product spectrum, 'zc' = zero crossings, NULL = no pitch analysis
pitchManual	manually corrected pitch contour. For a single sound, provide a numeric vector of any length. For multiple sounds, provide a dataframe with columns "file" and "pitch" (or path to a csv file) as returned by pitch_app, ideally with the same windowLength and step as in current call to analyze. A named list with pitch vectors per file is also accepted - e.g., as returned by pitch_app
pitchFloor, pitchCeiling	absolute bounds for pitch candidates (Hz)
priorMean, priorSD	specifies the mean (Hz) and standard deviation (semitones) of gamma distribution describing our prior knowledge about the most likely pitch values for this file. For ex., priorMean = 300, priorSD = 6 gives a prior with mean = 300 Hz and SD = 6 semitones (half an octave). NULL = no priors used at all; NA = no priors in the first pass, adaptive priors in the second pass if priorAdapt = TRUE
priorAdapt	adaptive second-pass prior: if TRUE, optimal pitch contours are estimated first with a prior determined by priorMean,priorSD, and then with a new prior adjusted according to this first-pass pitch contour
nCands	maximum number of pitch candidates per method, normally 1 to 4 (except for dom and hps, which return at most one candidate per frame)
minVoicedCands	minimum number of pitch candidates that have to be defined to consider a frame voiced (if NULL, defaults to 2 if dom is among other candidates and 1 otherwise)
pitchDom	a list of control parameters for pitch tracking using the lowest dominant frequency band or "dom" method
pitchAutocor	a list of control parameters for pitch tracking using the autocorrelation or "autocor" method
pitchCep	a list of control parameters for pitch tracking using the cepstrum or "cep" method
pitchSpec	a list of control parameters for pitch tracking using the BaNa or "spec" method
pitchHps	a list of control parameters for pitch tracking using the harmonic product spectrum or "hps" method
pitchZc	a list of control parameters for pitch tracking based on zero crossings in bandpass-filtered audio or "zc" method
harmHeight	a list of control parameters for estimating how high harmonics reach in the spectrum
subh	a list of control parameters for estimating the strength of subharmonics per frame - that is, spectral energy at integer fractions of f0: f0/2, f0/3, etc.
flux	a list of control parameters for calculating feature-based flux (not spectral flux)
amRange	target range of frequencies for amplitude modulation (amFreq, Hz): a vector of length 2, defaults to c(10, 60). Affects both amMsFreq and amEnvFreq. NB: f0 should not fall into this range, or AM will treat glottal cycles as modulation
fmRange	target range of frequencies for analyzing frequency modulation (fmFreq, Hz): a vector of length 2, defaults to c(5, 1000 / step / 2)
shortestSyl	the smallest length of a voiced segment (ms) that constitutes a voiced syllable (shorter segments will be replaced by NA, as if voiceless)
shortestPause	the smallest gap between voiced syllables (ms): large value = interpolate and merge, small value = treat as separate syllables separated by a voiceless gap; shortestPause < step disables any gap tolerance (a single voiceless frame terminates the syllable)
interpolPitch	a list of parameters (currently win, tol, cert) for interpolating missing pitch candidates (NULL = no interpolation)
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
smooth, 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 = 0
summaryFun	functions used to summarize each acoustic characteristic, eg c('mean', 'sd'); user-defined functions are fine (see examples); NAs are omitted automatically for mean/median/sd/min/max/range/sum, otherwise take care of NAs yourself
invalidArgAction	what to do if an argument is invalid or outside the permitted range: 'adjust' = reset to default value, 'abort' = stop execution, 'ignore' = throw a warning and continue (may crash)
reportEvery	when processing multiple inputs, report estimated time left every reportEvery iterations (NULL = default, NA = don't report); see reportTime
cores	number of cores for parallel processing
plot	if TRUE, produces a spectrogram with pitch contour overlaid
osc	"linear" = on the original scale (default); "none" = no oscillogram; "dB" = in decibels
showLegend	if TRUE, adds a legend with pitch tracking methods
savePlots	if TRUE, creates a subdirectory in the input directory (if input is a file or folder) or in the working directory (if input is a vector etc), named after the function (eg "spectrogram/"). All plots and audio files (if any) are saved in this new directory. If there are multiple inputs, an html notebook is also created for easy viewing and listening
embed	if TRUE and savePlots is set and there are multiple inputs, all saved images and audio (if any) are embedded in the exported html notebook for easy sharing; if FALSE, the html file links to separate images and audio files (but separate files are still saved). NB: for this to work, package "base64enc" must be installed
pitchPlot	a list of graphical parameters for displaying the final pitch contour. Set to list(type = 'n') to suppress
extraContour	name of an output variable to overlap on the pitch contour plot, eg 'peakFreq' or 'loudness'; can also be a list with extra graphical parameters, eg extraContour = list(x = 'harmHeight', col = 'red')
ylim	frequency range to plot, kHz (defaults to 0 to Nyquist frequency). NB: still in kHz, even if yScale = bark, mel, or ERB
xlab, ylab, main	plotting parameters
width, height, units, res	graphical parameters for saving plots passed to png
...	other graphical parameters passed to spectrogram

Details

Each pitch tracker is controlled by its own list of settings, as follows:

pitchDom (lowest dominant frequency band)

• 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

pitchAutocor (autocorrelation)

• autocorThres voicing threshold (unitless, ~0 to 1)

• 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

• autocorUpsample upsamples acf to this resolution (Hz) to improve accuracy in high frequencies

• autocorBestPeak amplitude of the lowest best candidate relative to the absolute max of the acf

• interpol method of interpolating the ACF: "sinc" for maximum precision, "none" for speed

pitchCep (cepstrum)

• cepThres voicing threshold (unitless, ~0 to 1)

• cepZp zero-padding of the spectrum used for cepstral pitch detection (final length of spectrum after zero-padding in points, e.g. 2 ^ 13)

pitchSpec (ratio of harmonics - BaNa algorithm)

• specThres voicing threshold (unitless, ~0 to 1)

• specPeak,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

• 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

• specMethod "commonFactor" = highest common factor of putative harmonics, "BaNa" = ratio of putative harmonics

• specRatios for method = "commonFactor", the number of harmonics and integer fractions to consider

pitchHps (harmonic product spectrum)

• hpsNum the number of times to downsample the spectrum

• hpsThres voicing threshold (unitless, ~0 to 1)

• hpsNorm the amount of inflation of hps pitch certainty (0 = none)

• hpsPenalty the amount of penalizing hps candidates in low frequencies (0 = none)

pitchZc (zero crossings)

• zcThres pitch candidates with certainty below this value are treated as noise and set to NA (0 = nothing discarded, 1 = pitch must be perfectly stable over zcWin)

• zcWin certainty in pitch candidates depends on how stable pitch is over zcWin glottal cycles (odd integer > 3)

Each of these lists also accepts graphical parameters that affect how pitch candidates are plotted, eg pitchDom = list(domThres = .5, col = 'yellow').

Other arguments that are lists of subroutine-specific settings include:

harmHeight (finding how high harmonics reach in the spectrum)

• harmThres minimum height of spectral peak, dB

• harmPerSel the number of harmonics per sliding selection

• harmTol maximum tolerated deviation of peak frequency from multiples of f0, proportion of f0

Value

A list with $detailed frame-by-frame descriptives and a $summary with one row per file, as determined by summaryFun (e.g., mean / median / SD of each acoustic variable across all STFT frames). Output measures include:

duration

total duration, s

duration_noSilence

duration from the beginning of the first non-silent STFT frame to the end of the last non-silent STFT frame, s (NB: depends strongly on windowLength and silence settings)

time

time of the middle of each frame (ms)

amEnvFreq,amEnvDep,amEnvPurity

frequency (Hz), purity (0 to 1), and depth (0 to 100) of amplitude modulation estimated from a smoothed amplitude envelope

amMsFreq,amMsPurity

frequency (Hz) and purity (dB) of amplitude modulation estimated via modulationSpectrum

ampl

root mean square of amplitude per frame, calculated as sqrt(mean(frame ^ 2))

ampl_noSilence

same as ampl, but ignoring silent frames

CPP

Cepstral Peak Prominence, dB (a measure of pitch quality, the ratio of the highest peak in the cepstrum to the regression line drawn through it)

dom

lowest dominant frequency band (Hz) (see "Pitch tracking methods / Dominant frequency" in the vignette)

entropyW

Wiener entropy of the spectrum of the current frame (=spectral flatness). Close to 0: pure tone or tonal sound with nearly all energy in harmonics; close to 1: white noise

entropySh

Normalized Shannon entropy of the spectrum of the current frame: 0 = pure tone, 1 = white noise

f1_freq, f1_width, ...

the frequency and bandwidth of the first nFormants formants per STFT frame, as calculated by phonTools::findformants

fluctuation

strength of low-frequency modulation at ~4 Hz (0.25-30 Hz), calculated from a modulation spectrum as a complement to psychoacoustic roughness; see modulationSpectrum

flux

feature-based flux, the rate of change in acoustic features such as pitch, HNR, etc. (0 = none, 1 = max); "epoch" is an audio segment between two peaks of flux that exceed a threshold of flux = list(thres = ...) (listed in output$detailed only)

fmFreq

frequency of frequency modulation (FM) such as vibrato or jitter, Hz

fmDep

depth of FM, semitones

fmPurity

purity or dominance of the main FM frequency (fmFreq), 0 to 1

harmEnergy

the amount of energy in upper harmonics, namely the ratio of total spectral mass above 1.25 x F0 to the total spectral mass below 1.25 x F0 (dB)

harmHeight

how high harmonics reach in the spectrum, based on the best guess at pitch (or the manually provided pitch values)

HNR

harmonics-to-noise ratio (dB), a measure of harmonicity (see "Pitch tracking methods / Autocorrelation"). If HNR = 0 dB, there is as much energy in harmonics as in noise

loudness

subjective loudness, in sone, corresponding to the chosen SPL_measured - see getLoudness

novelty

spectral novelty - a measure of how variable the spectrum is on a particular time scale, as estimated by ssm

peakFreq

the frequency with maximum spectral power (Hz)

pitch

post-processed pitch contour based on all F0 estimates

quartile25, quartile50, quartile75

the 25th, 50th, and 75th quantiles of the spectrum of voiced frames (Hz)

roughness

the amount of amplitude modulation in the roughness range, see modulationSpectrum and Anikin 2025

sharpness

psychoacoustic sharpness: related to spectral centroid, but calculated from a psychoacoustic loudness model, see getLoudness

specCentroid

the center of gravity of the frame's spectrum, first spectral moment (Hz)

specSlope

the slope of linear regression fit to the spectrum below cutFreq (dB/kHz)

subDep

estimated depth of subharmonics per frame: 0 = none, 1 = as strong as f0. NB: this depends critically on accurate pitch tracking

subRatio

the ratio of f0 to subharmonics frequency with strength subDep: 2 = period doubling, 3 = f0 / 3, etc.

voiced

is the current STFT frame voiced? TRUE / FALSE

References

Anikin, A. (2025) Acoustic estimation of voice roughness. Attention, Perception, & Psychophysics 87: 1771–1787.

See Also

pitch_app getLoudness segment getRMS

Examples

# Detailed documentation: https://cogsci.se/soundgen/acoustic_analysis.html

sound = soundgen(sylLen = 300, pitch = c(500, 400, 600),
  noise = list(time = c(0, 300), value = c(-40, 0)),
  temperature = 0.001,
  addSilence = 50)  # NB: always have some silence before and after!!!
# playme(sound, 16000)
a = analyze(sound, samplingRate = 16000, plot = TRUE)
str(a$detailed)  # frame-by-frame
a$summary        # summary per sound

## Not run: 
# For maximum processing speed (just basic spectral descriptives):
a = analyze(sound, samplingRate = 16000,
  plot = FALSE,         # no plotting
  pitchMethods = NULL,  # no pitch tracking
  loudness = NULL,      # no loudness analysis
  novelty = NULL,       # no novelty analysis
  roughness = NULL,     # no roughness analysis
  nFormants = 0         # no formant analysis
)

# Fancy plotting options:
a = analyze(sound, samplingRate = 44100, plot = TRUE,
  xlab = 'Time, ms', colorTheme = 'seewave', yScale = 'ERB',
  contrast = .5, ylim = c(0.05, 8), main = 'My plot',
  pitchMethods = c('dom', 'autocor', 'spec', 'hps', 'cep'),
  priorMean = NA,  # no prior info at all
  pitchDom = list(col = 'red', domThres = .25),
  pitchPlot = list(col = 'black', pch = 9, lty = 3, lwd = 3),
  extraContour = list(x = 'peakFreq', type = 'b', pch = 4, col = 'brown'),
  osc = 'dB', heights = c(2, 1))

# Analyze an entire folder in one go, saving spectrograms with pitch contours
# plus an html file for easy access
s2 = analyze('~/Downloads/temp',
  savePlots = TRUE,  # save the spectrograms with pitch contours
  showLegend = TRUE, yScale = 'bark',
  width = 20, height = 12,
  units = 'cm', res = 300, ylim = c(0, 5),
  cores = 4)  # use multiple cores to speed up processing
s2$summary[, 1:5]

# Analyzing ultrasounds (slow but possible, just adjust pitchCeiling)
s = soundgen(sylLen = 100, addSilence = 10,
  pitch = c(25000, 35000, 30000),
  formants = NA, rolloff = -12, rolloffKHz = 0,
  pitchSamplingRate = 350000, samplingRate = 350000, windowLength = 5,
  pitchCeiling = 45000, invalidArgAction = 'ignore',
  plot = TRUE)
# s is a bat-like ultrasound inaudible to humans

a = analyze(
  s, 350000, plot = TRUE,
  pitchFloor = 10000, pitchCeiling = 90000, priorMean = NA,
  pitchMethods = c('autocor', 'spec'),
  # probably shouldn't use pitchMethods = "dom" b/c of likely low-freq noise
  windowLength = 5, step = 2.5,
  shortestSyl = 10, shortestPause = 10,  # again, very short sounds
  interpolPitch = list(win = 10),  # again, very short sounds
  smooth = 0.1,  # might need less smoothing if very rapid f0 changes
  nFormants = 0, loudness = NULL, roughness = NULL, novelty = NULL)
# NB: ignore formants and loudness estimates for such non-human sounds

## End(Not run)

soundgen documentation built on Sept. 20, 2026, 5:07 p.m.