diffspec {seewave}

Difference between two frequency spectra

Description

This function estimates the surface difference between two frequency spectra.

Usage

diffspec(spec1, spec2, f = NULL, mel = FALSE,
plot = FALSE, type="l", 
lty=c(1, 2), col =c(2, 4, 8),
flab = NULL, alab = "Amplitude",
flim = NULL, alim = NULL, title = TRUE, legend = TRUE, ...)

Arguments

spec1

a first data set resulting of a spectral analysis obtained with spec or meanspec (not in dB). This can be either a two-column matrix (col1 = frequency, col2 = amplitude) or a vector (amplitude).

spec2

a first data set resulting of a spectral analysis obtained with spec or meanspec (not in dB). This can be either a two-column matrix (col1 = frequency, col2 = amplitude) or a vector (amplitude).

f

sampling frequency of waves used to obtain spec1 and spec2 (in Hz). Not necessary if spec1 and/or spec2 is a two-column matrix obtained with spec or meanspec.

mel

a logical, if TRUE the (htk-)mel scale is used.

plot

logical, if TRUE plots both spectra and their surface difference (by default FALSE).

type

if plot is TRUE, type of plot that should be drawn. See plot for details (by default "l" for lines).

lty

a vector of length 2 for the line type of spec1 and spec2 if type="l".

col

a vector of length 3 for the colour of spec1, spec2, and the surface difference between each of them.

flab

title of the frequency axis.

alab

title of the amplitude axis.

flim

the range of frequency values.

alim

range of amplitude axis.

title

logical, if TRUE, adds a title with D value.

legend

logical, if TRUE adds a legend to the plot.

...

other plot graphical parameters.

Details

D is a Manhattan distance (l1 norm).
Both spectra are first transformed as probability mass functions (PMF).
Spectral difference is then computed according to:

D = \frac{\sum{|spec1-spec2|}}{2}, with D \in [0,1].

, with 0 < D < 1.

Value

The difference is returned. This value is without unit. When plot is TRUE, both spectra and their difference surface are plotted on the same graph.

Note

This method can be used as a relative distance estimation between different spectra.
The dB value obtained can be very different from the one visually estimated when looking at the graph (plot=TRUE).

Author(s)

Jerome Sueur, Sandrine Pavoine and Laurent Lellouch

References

Sueur, J., Pavoine, S., Hamerlynck, O. and Duvail, S. (2008). Rapid acoustic survey for biodiversity appraisal. PLoS One, 3(12): e4065.

See Also

spec, meanspec, corspec, simspec, diffcumspec, diffenv, kl.dist, ks.dist, logspec.dist, itakura.dist

Examples

a <- noisew(f=8000,d=1)
b <- synth(f=8000,d=1,cf=2000)
c <- synth(f=8000,d=1,cf=1000)
d <- noisew(f=8000,d=1)
speca <- spec(a,f=8000,wl=512,at=0.5,plot=FALSE)
specb <- spec(b,f=8000,wl=512,at=0.5,plot=FALSE)
specc <- spec(c,f=8000,wl=512,at=0.5,plot=FALSE)
specd <- spec(d,f=8000,wl=512,at=0.5,plot=FALSE)
diffspec(speca,speca,f=8000)
#[1] 0 => similar spectra of course !
diffspec(speca,specb)
diffspec(speca,specc,plot=TRUE)
diffspec(specb,specc,plot=TRUE)
diffspec(speca,specd,plot=TRUE)
## mel scale
require(tuneR)
data(orni)
data(tico)
orni.mel <- melfcc(orni, nbands = 256, dcttype = "t3", fbtype = "htkmel", spec_out=TRUE)
orni.mel.mean <- apply(orni.mel$aspectrum, MARGIN=2, FUN=mean)
tico.mel <- melfcc(tico, nbands = 256, dcttype = "t3", fbtype = "htkmel", spec_out=TRUE)
tico.mel.mean <- apply(tico.mel$aspectrum, MARGIN=2, FUN=mean)
diffspec(orni.mel.mean, tico.mel.mean, f=22050, mel=TRUE, plot=TRUE)