# Similarity between two frequency spectra This function estimates the similarity between two frequency spectra. ```r 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: $$ S = \frac{100/N} \times{\sum_{i=1}^N{\frac{\min{spec1(i),spec2(i)}}{\max{spec1(i),spec2(i)}}}}%S = 100*sum(min(spec1,spec2)/max(spec1,spec2))/N, with \emph{S} in %. $$ with **S** in %. ## Returns 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. ## 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. ## Author(s) Jerome Sueur, improved by Laurent Lellouch ## See Also `spec`, `meanspec`, `corspec`, `diffspec`, `diffenv`, `kl.dist`, `ks.dist`, `logspec.dist`, `itakura.dist` ## Examples ```r 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) ```

simspec function