TFSD: normalized Time and Frequency Second Derivative

In seewave: Sound Analysis and Synthesis

normalized Time and Frequency Second Derivative

Description

This function computes the normalized Time and Frequency Second Derivative as described by Aumond et al. (2017).

Usage

TFSD(wave, f, channel = 1, ovlp = 0,  wn = "hamming", flim = c(2,6), nbwindows = 1)

Arguments

wave	an R object.
f	sampling frequency of wave (in Hz). Does not need to be specified if embedded in wave.
channel	channel of the R object, by default left channel (1).
ovlp	overlap between two successive windows (in %).
wn	window name, see ftwindow (by default "hanning").
flim	a numeric vector of length 2 to select a frequency band (in kHz). Cannot be NULL.
nbwindows	a numeric vector of length 1 specifying the number of windows (by default 1, ie a single window including the complete wave object.

Details

The TFSD aims at estimating the time of presence of avian or human vocalizations within a sound environment. It calculates the variation in time and frequency of a signal around frequencies of interest, normalized by the spectral time variation of a signal as a whole.

Warning, this index was initially developed to work from a third octave spectrogram with a time sampling of 125 ms.

TFSD is computed according to formulation in reference.

The higher the TFSD varies between 0 and 1, the greater the temporal presence of avian or human vocalizations. With the default configuration, a TFSD > 0.3 indicates a very important presence time of the vocalizations in the signal. The TFSD is always greater than 0.

Value

A numeric vector of length nbwindows giving the TFSD values.

Author(s)

Pierre Aumond, Guillaume Corbeau

References

Aumond, P., Can, A., De Coensel, B., Botteldooren, D., Ribeiro, C., & Lavandier, C. (2017). Modeling soundscape pleasantness using perceptual assessments and acoustic measurements along paths in urban context. Acta Acustica united with Acustica, 12, 50-67.

Gontier, F., Lavandier, C., Aumond, P., Lagrange, M., & Petiot, J. F. (2019). Estimation of the perceived time of presence of sources in urban acoustic environments using deep learning techniques. Acta Acustica united with Acustica, 105(6), 1053-1066.

See Also

ACI, NDSI

Examples

## Note that 'tico' is not a soundscape recording...
data(tico)
TFSD(tico)
## dividing the sound sample into 4 windows of equal duration
TFSD(tico, nbwindows=4)
## selection of a frequency band
TFSD(tico, flim=c(2,6))

seewave documentation built on Oct. 19, 2023, 5:07 p.m.