Rugosity of a time wave
This function computes the rugosity of a time wave or time series
rugo(x, ...)
x |
a vector |
... |
other |
The formula has been slightly modified from Mezquida & Martinez (2009:
826) to fit with the classical definition of the root-mean-square
(see rms).
The rugosity is then computed as following:
rugo = sqrt(mean((x[i+1]-x[i])^2))
for a vector x of length n.
A vector of length 1.
The rugosity of a noisy signal will tend to be higher than that of
a pure tone signal, all other things being equal.
Jerome Sueur
Mezquida DA, Martinez JL (2009) - Platform for bee-hives monitoring based on sound analysis. A perpetual warehouse for swarm's daily activity. Spanish Journal of Agricultural Research 7, 824-828.
data(tico) ; tico <-tico@left # rugosity of the original recording normalised rugo(tico/max(tico)) # synthesis of white noise with the same duration as tico noise <- noisew(d=length(tico)/22050, f=22050) # tico is normalised to get similar amplitude with the noise tico.norm <- tico/max(tico) # addition of noise to tico tico.noisy <- tico.norm + 0.5*noise # new rugosity (higher) on normalised signal rugo(tico.noisy/max(tico.noisy))
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