Continuous spectral entropy
This function computes the continuous spectral entropy (H) of a time wave.
csh(wave, f, channel = 1, wl = 512, wn = "hanning", ovlp = 0, fftw = FALSE, threshold = NULL, plot = TRUE, xlab = "Times (s)", ylab = "Spectral Entropy", ylim = c(0, 1.1), type = "l", ...)
wave |
an R object. |
f |
sampling frequency of |
channel |
channel of the R object, by default left channel (1). |
wl |
if |
wn |
window name, see |
ovlp |
overlap between two successive windows (in %). |
fftw |
if |
threshold |
amplitude threshold for signal detection (in %). |
plot |
logical, if |
xlab |
title of the x axis. |
ylab |
title of the y axis. |
ylim |
the range of y values. |
type |
if |
... |
other |
See sh for computing method.
When plot is FALSE, csh returns a two-column matrix, the
first column being time in seconds (x-axis) and the second column being the spectral entropy (y-axis) computed along time.
NA corresponds to pause sections in wave (see threshold).
The spectral entropy of a noisy signal will tend towards 1 whereas the spectral entropy of a pure tone signal will tend towards 0.
Jerome Sueur sueur@mnhn.fr
Toh, A. M., Togneri, R. & Nordholm, S. 2005 Spectral entropy as speech features for speech recognition. Proceedings of PEECS, pp. 60-65.
data(orni) csh(orni,f=22050,wl=512,ovlp=50) # using the threshold argument can lead to some edge effets # here sh=1 at the end of echemes csh(orni,f=22050,wl=512,ovlp=50,threshold=5)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.