signal.package: Signal processing

Description Details Author(s) References Examples

Description

A set of generally Matlab/Octave-compatible signal processing functions. Includes filter generation utilities, filtering functions, resampling routines, and visualization of filter models. It also includes interpolation functions and some Matlab compatibility functions.

Details

The main routines are:

Filtering: filter, fftfilt, filtfilt, medfilt1, sgolay, sgolayfilt

Resampling: interp, resample, decimate

IIR filter design: bilinear, butter, buttord, cheb1ord, cheb2ord, cheby1, cheby2, ellip, ellipord, sftrans

FIR filter design: fir1, fir2, remez, kaiserord, spencer

Interpolation: interp1, pchip

Compatibility routines and utilities: ifft, sinc, postpad, chirp, poly, polyval

Windowing: bartlett, blackman, boxcar, flattopwin, gausswin, hamming, hanning, triang

Analysis and visualization: freqs, freqz, impz, zplane, grpdelay, specgram

Most of the functions accept Matlab-compatible argument lists, but many are generic functions and can accept simpler argument lists.

For a complete list, use library(help="signal").

Author(s)

Most of these routines were translated from Octave Forge routines. The main credit goes to the original Octave authors:

Paul Kienzle, John W. Eaton, Kurt Hornik, Andreas Weingessel, Kai Habel, Julius O. Smith III, Bill Lash, André Carezia, Paulo Neis, David Billinghurst, Friedrich Leisch

Translations by Tom Short tshort@eprisolutions.com (who maintained the package until 2009).

Current maintainer is Uwe Ligges ligges@statistik.tu-dortmund.de.

References

https://en.wikipedia.org/wiki/Category:Signal_processing

Octave Forge https://octave.sourceforge.io/

Package matlab by P. Roebuck

For Matlab/Octave conversion and compatibility, see http://mathesaurus.sourceforge.net/octave-r.html by Vidar Bronken Gundersen and https://cran.r-project.org/doc/contrib/R-and-octave.txt by Robin Hankin.

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
## The R implementation of these routines can be called "matlab-style",
bf <- butter(5, 0.2)
freqz(bf$b, bf$a)
## or "R-style" as:
freqz(bf)

## make a Chebyshev type II filter:
ch <- cheby2(5, 20, 0.2) 
freqz(ch, Fs = 100)  # frequency plot for a sample rate = 100 Hz

zplane(ch) # look at the poles and zeros

## apply the filter to a signal
t <- seq(0, 1, by = 0.01)                     # 1 second sample, Fs = 100 Hz
x <- sin(2*pi*t*2.3) + 0.25*rnorm(length(t))  # 2.3 Hz sinusoid+noise
z <- filter(ch, x)  # apply filter
plot(t, x, type = "l")
lines(t, z, col = "red")

# look at the group delay as a function of frequency
grpdelay(ch, Fs = 100)

Example output

Attaching package: 'signal'

The following objects are masked from 'package:stats':

    filter, poly

- Group delay (gd) calculated at 512 points.
- Frequencies (w) given in *Hz*. 
       gd          w
 3.162407 0.00000000
 3.162772 0.09765625
 3.163869 0.19531250
 3.165699 0.29296875
 .......  .......
Warning message:
In grpdelay.default(filt$b, filt$a, ...) :
  grpdelay: setting group delay to 0 at singularity

signal documentation built on May 25, 2021, 9:06 a.m.