seewave (version 2.2.3)

TFSD: 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)

Value

A numeric vector of length nbwindows giving the TFSD values.

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.

Author

Pierre Aumond, Guillaume Corbeau

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.

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))