R: Similarity between two frequency spectra

simspec {seewave}

R Documentation

Similarity between two frequency spectra

Description

This function estimates the similarity between two frequency spectra.

Usage

simspec(spec1, spec2, f = NULL, mel = FALSE,
norm = FALSE, PMF = FALSE,
plot = FALSE, type = "l",
lty =c(1, 2, 3), col = c(2, 4, 1),
flab = NULL, alab = "Amplitude (percentage)",
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 columns matrix obtained with `spec` or `meanspec`.
`mel`	a logical, if `TRUE` the (htk-)mel scale is used.
`norm`	a logical, if `TRUE` `spec1` and `spec2` are normalised (scaled) between 0 and 1.
`PMF`	a logical, if `TRUE` `spec1` and `spec2` are transformed into probability mass functions.
`plot`	logical, if `TRUE` plots both spectra and similarity function (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 3 for the line type of `spec1`, `spec2` and of the similarity function if `type`="l".
`col`	a vector of length 3 for the colour of `spec1`, `spec2`, and the similarity function.
`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 S value.
`legend`	logical, if `TRUE` adds a legend to the plot.
`...`	other `plot` graphical parameters.

Details

Spectra similarity is assessed according to:

S = 100*sum(min(spec1,spec2)/max(spec1,spec2))/N, with \emph{S} in %.

with S in %.

Value

The similarity index is returned. This value is in %.
When plot is TRUE, both spectra and the similarity function are plotted on the same graph. The similarity index is the mean of this function.

Author(s)

Jerome Sueur, improved by Laurent Lellouch

References

Deecke, V. B. and Janik, V. M. 2006. Automated categorization of bioacoustic signals: avoiding perceptual pitfalls. Journal of the Acoustical Society of America, 119: 645-653.

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,at=0.5,plot=FALSE)
specb<-spec(b,f=8000,at=0.5,plot=FALSE)
specc<-spec(c,f=8000,at=0.5,plot=FALSE)
specd<-spec(d,f=8000,at=0.5,plot=FALSE)
simspec(speca,speca)
simspec(speca,specb)
simspec(speca,specc,plot=TRUE)
simspec(specb,specc,plot=TRUE)
#[1] 12.05652
simspec(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)
simspec(orni.mel.mean, tico.mel.mean, f=22050, mel=TRUE, plot=TRUE)

[Package seewave version 2.1.6 Index]