scd: Soundcape chord diagram

In seewave: Sound Analysis and Synthesis

Soundcape chord diagram

Description

Computes and displays a chord diagram of a set of audio files or of a set segments extracted from a single audio file.

Usage

scd(input, f,  sl, wl = 512, wn = "hanning", ovlp = 0, flim = NULL,
rmoffset = TRUE, threshold = NULL, HCA = TRUE,
grid.col = terrain.colors, names, plot = TRUE, verbose = TRUE, ...)

Arguments

input	a character vector, either (i) the path to the directory where .wav files are stored, (ii) directly the names of the .wav files to be processed, or (iii) a single .wav file to be segmented by the duration set with the argument sl.
f	sampling frequency of wave (in Hz). Does not need to be specified if embedded in the .wav files contained in input.
sl	segment length in duration if input is a single file.
wl	window length for the analysis spectral (even number of points) (by default = 512).
wn	window name for the spectral analysis, see ftwindow (by default "hanning").
ovlp	overlap between two successive windows (in %) for the spectral analysis.
flim	a numeric vector of length 2 to select a frequency band (in kHz).
rmoffset	a logical to sepcify whether DC offset should be removed. By default TRUE.
threshold	a numeric value in ]0,1[ to be applied to the similarity distance. All similairty distances below this threshold will not depicted.
HCA	logical, if TRUE the sectors are colored according to clusters automatically obtained with a hierarchical cluster analysis (HCA).
grid.col	name of color palette to color the sectors and the links). By default terrain.colors.
names	names of the sectors, if empty then the names of the .wav files or the time stamps of the segments.
plot	logical, if TRUE plots the chord diagram.
verbose	logical, if TRUE prints the computation progress of the mean spectra.
...	other chordDiagram parameters.

Details

The soundscape chord diagram (SCD) aims at representing similarities between audio files or audio segments extracted from a single audio file. The mean frequency spectrum of each file/segment is computed using a STFT. These frequency spectra are then (1) pairwised compared using a similarity distance (see function diffcumspec, and (2) automatically clustered with a hierarchical cluster analysis (HCA) (see function HCPC of FactoMiner). The resulting similarity matrix is then given as an input to the function chordDiagram. The width of the sectors and the links are based on the spectral similarity matrix. The color of the sectors and the links follow the HCA classification.

Value

THe function returns a list of two items:

m	spectral similarity matrix
resHCA	the classification result of the HCA, if HCA is TRUE

Note

The function call the function HCPC of the package FactoMineR and the function chordDiagram of the package circlize.

Author(s)

Adèle de Baudouin, Jérôme Sueur

References

de Baudouin, A, Couprie P, Michaud F, Haupert S, Sueur J – Similarity visualization of nature and music soundscapes, in prep.

See Also

diffcumspec

Examples

## Not run: 
## 1 ##
# if 'dir' contains a set of files recorded with a Wildlife Acoustics
# songmeter recorder or an Audiomoth  then a direct way to obtain
# the soundscape chord diagram (SCD)  of all .wav files is
dir <- "pathway-to-directory-containing-wav-files"
scd(dir)
# to change the STFT parameters used to obtain each mean spectrum
lts(dir, wl=1024, wn="hamming", ovlp=50)
# to select only high similarities, here above 0.6
scd(dir, threshold=0.6)
# to change the colors
scd(dir, grid.col=colorRampPalette(c("darkblue", "yellow", "grey")))
# to name manually the sectors
scd(dir, names=as.character(0:23))
# to name automatically the sectors from the name of songmeter files
# here according to hour of recording 
scd(dir, names=as.character(songmeter(files)$hour))  

## 2 ##
# to directly use files names stored in the working directory
files <- c("S4A09154_20190213_150000.wav", "S4A09154_20190213_153000.wav",
"S4A09154_20190213_160000.wav", "S4A09154_20190213_163000.wav",
"S4A09154_20190213_170000.wav", "S4A09154_20190213_173000.wav",
"S4A09154_20190213_180000.wav", "S4A09154_20190213_183000.wav",
"S4A09154_20190213_190000.wav", "S4A09154_20190213_193000.wav")
scd(files)

## 3 ##
# to  use of single files which is segmented in successive time segments
# lasting each 60 s
file <- "a-very-nice-soundscape.wav")
scd(file, sl = 60)

## End(Not run)

seewave documentation built on Oct. 19, 2023, 5:07 p.m.