getPitchZc: Zero-crossing rate

Description

A less precise, but very quick method of pitch tracking based on measuring zero-crossing rate in low-pass-filtered audio. Recommended for processing long recordings with typical pitch values well below the first formant frequency, such as speech. Calling this function is considerably faster than using the same pitch-tracking method in analyze. Note that, unlike analyze(), it returns the times of individual zero crossings (hopefully corresponding to glottal cycles) instead of pitch values at fixed time intervals.

Usage

getPitchZc(
  x,
  samplingRate = NULL,
  scale = NULL,
  from = NULL,
  to = NULL,
  pitchFloor = 50,
  pitchCeiling = 400,
  zcThres = 0.1,
  zcWin = 5,
  silence = 0.04,
  envWin = 5,
  summaryFun = c("mean", "sd"),
  reportEvery = NULL
)

Value

Returns a dataframe containing

pitch: pitch calculated from the time between consecutive zero crossings
cert: certainty in each pitch candidate calculated from local pitch stability, 0 to 1

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 for normalization of input vector (only needed if x is a numeric vector)
from: if NULL (default), analyzes the whole sound, otherwise from...to (s)
to: if NULL (default), analyzes the whole sound, otherwise from...to (s)
pitchFloor: absolute bounds for pitch candidates (Hz)
pitchCeiling: absolute bounds for pitch candidates (Hz)
zcThres: pitch candidates with certainty below this value are treated as noise and set to NA (0 = anything goes, 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)
silence: minimum root mean square (RMS) amplitude, below which pitch candidates are set to NA (NULL = don't consider RMS amplitude)
envWin: window length for calculating RMS envelope, ms
summaryFun: functions used to summarize each acoustic characteristic; see analyze
reportEvery: when processing multiple inputs, report estimated time left every ... iterations (NULL = default, NA = don't report)

Details

Algorithm: the audio is bandpass-filtered from pitchFloor to pitchCeiling, and the timing of all zero crossings is saved. This is not enough, however, because unvoiced sounds like white noise also have plenty of zero crossings. Accordingly, an attempt is made to detect voiced segments (or steady musical tones, etc.) by looking for stable regions, with several zero-crossings at relatively regular intervals (see parameters zcThres and zcWin). Very quiet parts of audio are also treated as not having a pitch.

Examples

Run this code

data(sheep, package = 'seewave')
# spectrogram(sheep)
zc = getPitchZc(sheep, pitchCeiling = 250)
plot(zc$detailed[, c('time', 'pitch')], type = 'b')

# Convert to a standard pitch contour sampled at regular time intervals:
pitch = getSmoothContour(
  anchors = data.frame(time = zc$detailed$time, value = zc$detailed$pitch),
  len = 1000, NA_to_zero = FALSE, discontThres = 0)
spectrogram(sheep, extraContour = pitch, ylim = c(0, 2))

if (FALSE) {
# process all files in a folder
zc = getPitchZc('~/Downloads/temp')
zc$summary
}