mfcc_stats: Calculate descriptive statistics on Mel-frequency cepstral...

In warbleR: Streamline Bioacoustic Analysis

Calculate descriptive statistics on Mel-frequency cepstral coefficients

Description

mfcc_stats calculates descriptive statistics on Mel-frequency cepstral coefficients and its derivatives.

Usage

mfcc_stats(
  X,
  ovlp = 50,
  wl = 512,
  bp = "frange",
  path = NULL,
  numcep = 25,
  nbands = 40,
  parallel = 1,
  pb = TRUE,
  ...
)

Arguments

X	'selection_table', 'extended_selection_table' or data frame with the following columns: 1) "sound.files": name of the sound files, 2) "sel": number of the selections, 3) "start": start time of selections, 4) "end": end time of selections.
ovlp	Numeric vector of length 1 specifying % of overlap between two consecutive windows. Internally this is used to set the 'hoptime' argument in melfcc. Default is 50.
wl	A numeric vector of length 1 specifying the spectrogram window length. Default is 512. See 'wl.freq' for setting windows length independently in the frequency domain.
bp	A numeric vector of length 2 for the lower and upper limits of a frequency bandpass filter (in kHz) or "frange" (default) to indicate that values in minimum of 'bottom.freq' and maximum of 'top.freq' columns will be used as bandpass limits.
path	Character string containing the directory path where the sound files are located.
numcep	Numeric vector of length 1 controlling the number of cepstra to return (see melfcc).
nbands	Numeric vector of length 1 controlling the number of warped spectral bands to use (see melfcc). Default is 40.
parallel	Numeric. Controls whether parallel computing is applied. It specifies the number of cores to be used. Default is 1 (i.e. no parallel computing).
pb	Logical argument to control progress bar and messages. Default is TRUE.
...	Additional parameters to be passed to melfcc.

Details

The function calculates descriptive statistics on Mel-frequency cepstral coefficients (MFCCs) for each of the signals (rows) in a selection data frame. The descriptive statistics are: minimum, maximum, mean, median, skewness, kurtosis and variance. It also returns the mean and variance for the first and second derivatives of the coefficients. These parameters are commonly used in acoustic signal processing and detection (e.g. Salamon et al 2014).

Value

A data frame containing the descriptive statistics for each of the Mel-frequency cepstral coefficients (set by 'numcep' argument). See details.

Author(s)

Marcelo Araya-Salas ([e-mail])

References

Araya-Salas, M., & Smith-Vidaurre, G. (2017). warbleR: An R package to streamline analysis of animal acoustic signals. Methods in Ecology and Evolution, 8(2), 184-191.

Lyon, R. H., & Ordubadi, A. (1982). Use of cepstra in acoustical signal analysis. Journal of Mechanical Design, 104(2), 303-306.

Salamon, J., Jacoby, C., & Bello, J. P. (2014). A dataset and taxonomy for urban sound research. In Proceedings of the 22nd ACM international conference on Multimedia. 1041-1044.

See Also

fix_wavs, remove_silence, spectro_analysis

Examples

{
data(list = c("Phae.long1", "Phae.long2", "Phae.long3", "Phae.long4", "lbh_selec_table"))
writeWave(Phae.long1, file.path(tempdir(), "Phae.long1.wav"))
writeWave(Phae.long2, file.path(tempdir(), "Phae.long2.wav"))
writeWave(Phae.long3, file.path(tempdir(), "Phae.long3.wav"))
writeWave(Phae.long4, file.path(tempdir(), "Phae.long4.wav"))

# run function
mel_st <- mfcc_stats(X = lbh_selec_table, pb = FALSE, path = tempdir())

head(mel_st)

# measure 12 coefficients
mel_st12 <- mfcc_stats(X = lbh_selec_table, numcep = 12, pb = FALSE, path = tempdir())

 head(mel_st)
}

warbleR documentation built on Sept. 11, 2024, 7:13 p.m.