seewave (version 1.6.3)

corspec: Cross-correlation between two frequency spectra

Description

This function tests the similarity between two frequency spectra by returning their maximal correlation and the frequency shift related to it.

Usage

corspec(spec1, spec2, f = NULL, plot = TRUE, plotval = TRUE,
method = "spearman", col = "black", colval = "red",
cexval = 1, fontval = 1, xlab = "Frequency (kHz)",
ylab = "Coefficient of correlation (r)", type="l",...)

Value

• If plot is FALSE, corspec returns a list containing four components:
• r
  a two-column matrix, the first colum corresponding to the frequency shift (frequency x-axis) and the second column corresponding to the successive r correlation values between spec1 and spec2 (correlation y-axis).
• rmax
  the maximum correlation value between spec1 and spec2.
• p
  the p value corresponding to rmax.
• f
  the frequency offset corresponding to rmax.

Details

It is important not to have data in dB. Successive correlations between spec1 and spec2 are computed when regularly shifting spec2 towards lower or higher frequencies. The maximal correlation is obtained at a particular shift (frequency offset). This shift may be positive or negative. The corresponding p value, obtained with cor.test, is plotted. Inverting spec1 and spec2 may give slight different results, see examples.

References

Hopp, S. L., Owren, M. J. and Evans, C. S. (Eds) 1998. Animal acoustic communication. Springer, Berlin, Heidelberg.

See Also

spec, meanspec, corspec, covspectro, cor, cor.test.

Examples

data(tico)
# compare the two first notes spectra
a<-spec(tico,f=22050,wl=512,at=0.2,plot=FALSE)
c<-spec(tico,f=22050,wl=512,at=1.1,plot=FALSE)
op<-par(mfrow=c(2,1), mar=c(4.5,4,3,1))
spec(tico,f=22050,at=0.2,col="blue")
par(new=TRUE)
spec(tico,f=22050,at=1.1,col="green")
legend(x=8,y=0.5,c("Note A", "Note C"),lty=1,col=c("blue","green"),bty="o")
par(mar=c(5,4,2,1))
corspec(a,c, ylim=c(-0.25,0.8),xaxs="i",yaxs="i",las=1)
par(op)
# different correlation methods give different results...
op<-par(mfrow=c(3,1))
corspec(a,c,xaxs="i",las=1, ylim=c(-0.25,0.8))
title("spearmann correlation (by default)")
corspec(a,c,xaxs="i",las=1,ylim=c(0,1),method="pearson")
title("pearson correlation")
corspec(a,c,xaxs="i",las=1,ylim=c(-0.23,0.5),method="kendall")
title("kendall correlation")
par(op)
# inverting x and y does not give exactly similar results
op<-par(mfrow=c(2,1),mar=c(2,4,3,1))
corspec(a,c)
corspec(c,a)
par(op)