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Signal Processing

I hope that someday Octave will include more signal processing functions. If you would like to help improve Octave in this area, please contact bug-octave@bevo.che.wisc.edu.

@anchor{doc-detrend}

Function File: detrend (x, p)

If x is a vector, detrend (x, p) removes the best fit of a polynomial of order p from the data x.

If x is a matrix, detrend (x, p) does the same for each column in x.

The second argument is optional. If it is not specified, a value of 1 is assumed. This corresponds to removing a linear trend.

@anchor{doc-fft}

Loadable Function: fft (a, n)

Compute the FFT of a using subroutines from FFTPACK. If a is a matrix, fft computes the FFT for each column of a.

If called with two arguments, n is expected to be an integer specifying the number of elements of a to use. If a is a matrix, n specifies the number of rows of a to use. If n is larger than the size of a, a is resized and padded with zeros.

@anchor{doc-ifft}

Loadable Function: ifft (a, n)

Compute the inverse FFT of a using subroutines from FFTPACK. If a is a matrix, fft computes the inverse FFT for each column of a.

@anchor{doc-fft2}

Loadable Function: fft2 (a, n, m)

Compute the two dimensional FFT of a.

The optional arguments n and m may be used specify the number of rows and columns of a to use. If either of these is larger than the size of a, a is resized and padded with zeros.

@anchor{doc-ifft2}

Loadable Function: ifft2 (a, n, m)

Compute the two dimensional inverse FFT of a.

The optional arguments n and m may be used specify the number of rows and columns of a to use. If either of these is larger than the size of a, a is resized and padded with zeros.

@anchor{doc-fftconv}

Function File: fftconv (a, b, n)

Return the convolution of the vectors a and b, as a vector with length equal to the length (a) + length (b) - 1. If a and b are the coefficient vectors of two polynomials, the returned value is the coefficient vector of the product polynomial.

The computation uses the FFT by calling the function fftfilt. If the optional argument n is specified, an N-point FFT is used.

@anchor{doc-fftfilt}

Function File: fftfilt (b, x, n)

With two arguments, fftfilt filters x with the FIR filter b using the FFT.

Given the optional third argument, n, fftfilt uses the overlap-add method to filter x with b using an N-point FFT.

If x is a matrix, filter each column of the matrix.

@anchor{doc-filter}

Loadable Function: y = filter (b, a, x)

Loadable Function: [y, sf] = filter (b, a, x, si)

Return the solution to the following linear, time-invariant difference equation:

   N                   M
  SUM a(k+1) y(n-k) = SUM b(k+1) x(n-k)      for 1<=n<=length(x)
  k=0                 k=0

where N=length(a)-1 and M=length(b)-1. An equivalent form of this equation is:

            N                   M
  y(n) = - SUM c(k+1) y(n-k) + SUM d(k+1) x(n-k)  for 1<=n<=length(x)
           k=1                 k=0

where c = a/a(1) and d = b/a(1).

If the fourth argument si is provided, it is taken as the initial state of the system and the final state is returned as sf. The state vector is a column vector whose length is equal to the length of the longest coefficient vector minus one. If si is not supplied, the initial state vector is set to all zeros.

In terms of the z-transform, y is the result of passing the discrete- time signal x through a system characterized by the following rational system function:

             M
            SUM d(k+1) z^(-k)
            k=0
  H(z) = ----------------------
               N
          1 + SUM c(k+1) z(-k)
              k=1

@anchor{doc-freqz}

Function File: [h, w] = freqz (b, a, n, "whole")

Return the complex frequency response h of the rational IIR filter whose numerator and denominator coefficients are b and a, respectively. The response is evaluated at n angular frequencies between 0 and 2*pi.

The output value w is a vector of the frequencies.

If the fourth argument is omitted, the response is evaluated at frequencies between 0 and pi.

If n is omitted, a value of 512 is assumed.

If a is omitted, the denominator is assumed to be 1 (this corresponds to a simple FIR filter).

For fastest computation, n should factor into a small number of small primes.

Function File: h = freqz (b, a, w)

Evaluate the response at the specific frequencies in the vector w. The values for w are measured in radians.

Function File: [...] = freqz (..., Fs)

Return frequencies in Hz instead of radians assuming a sampling rate Fs. If you are evaluating the response at specific frequencies w, those frequencies should be requested in Hz rather than radians.

Function File: freqz (...)

Plot the pass band, stop band and phase response of h rather than returning them.

@anchor{doc-freqz_plot}

Function File: freqz_plot (w, h): Plot the pass band, stop band and phase response of h.

@anchor{doc-sinc}

Function File: sinc (x): Return sin(pi*x)/(pi*x).

@anchor{doc-unwrap}

Function File: b = unwrap (a, tol, dim)

Unwrap radian phases by adding multiples of 2*pi as appropriate to remove jumps greater than tol. tol defaults to pi.

Unwrap will unwrap along the columns of a unless the row dimension of a is 1 or dim is given with a value of 1, when it will unwrap along the row(s).

@anchor{doc-arch_fit}

Function File: [a, b] = arch_fit (y, x, p, iter, gamma, a0, b0)

Fit an ARCH regression model to the time series y using the scoring algorithm in Engle's original ARCH paper. The model is

y(t) = b(1) * x(t,1) + ... + b(k) * x(t,k) + e(t),
h(t) = a(1) + a(2) * e(t-1)^2 + ... + a(p+1) * e(t-p)^2

in which @math{e(t)} is @math{N(0, h(t))}, given a time-series vector y up to time @math{t-1} and a matrix of (ordinary) regressors x up to @math{t}. The order of the regression of the residual variance is specified by p.

If invoked as arch_fit (y, k, p) with a positive integer k, fit an ARCH(k, p) process, i.e., do the above with the @math{t}-th row of x given by

[1, y(t-1), ..., y(t-k)]

Optionally, one can specify the number of iterations iter, the updating factor gamma, and initial values @math{a0} and @math{b0} for the scoring algorithm.

@anchor{doc-arch_rnd}

Function File: arch_rnd (a, b, t)

Simulate an ARCH sequence of length t with AR coefficients b and CH coefficients a. I.e., the result @math{y(t)} follows the model

y(t) = b(1) + b(2) * y(t-1) + ... + b(lb) * y(t-lb+1) + e(t),

where @math{e(t)}, given y up to time @math{t-1}, is @math{N(0, h(t))}, with

h(t) = a(1) + a(2) * e(t-1)^2 + ... + a(la) * e(t-la+1)^2

@anchor{doc-arch_test}

Function File: [pval, lm] = arch_test (y, x, p)

For a linear regression model

y = x * b + e

perform a Lagrange Multiplier (LM) test of the null hypothesis of no conditional heteroscedascity against the alternative of CH(p).

I.e., the model is

y(t) = b(1) * x(t,1) + ... + b(k) * x(t,k) + e(t),

given y up to @math{t-1} and x up to @math{t}, @math{e}(t) is @math{N(0, h(t))} with

h(t) = v + a(1) * e(t-1)^2 + ... + a(p) * e(t-p)^2,

and the null is @math{a(1)} == ... == @math{a(p)} == 0.

If the second argument is a scalar integer, @math{k}, perform the same test in a linear autoregression model of order @math{k}, i.e., with

[1, y(t-1), ..., y(t-k)]

as the @math{t}-th row of x.

Under the null, LM approximately has a chisquare distribution with p degrees of freedom and pval is the @math{p}-value (1 minus the CDF of this distribution at LM) of the test.

If no output argument is given, the @math{p}-value is displayed.

@anchor{doc-arma_rnd}

Function File: arma_rnd (a, b, v, t, n)

Return a simulation of the ARMA model

x(n) = a(1) * x(n-1) + ... + a(k) * x(n-k)
     + e(n) + b(1) * e(n-1) + ... + b(l) * e(n-l)

in which k is the length of vector a, l is the length of vector b and e is gaussian white noise with variance v. The function returns a vector of length t.

The optional parameter n gives the number of dummy x(i) used for initialization, i.e., a sequence of length t+n is generated and x(n+1:t+n) is returned. If n is omitted, n = 100 is used.

@anchor{doc-autocor}

Function File: autocor (x, h): Return the autocorrelations from lag 0 to h of vector x. If h is omitted, all autocorrelations are computed. If x is a matrix, the autocorrelations of each column are computed.

@anchor{doc-autocov}

Function File: autocov (x, h): Return the autocovariances from lag 0 to h of vector x. If h is omitted, all autocovariances are computed. If x is a matrix, the autocovariances of each column are computed.

@anchor{doc-autoreg_matrix}

Function File: autoreg_matrix (y, k): Given a time series (vector) y, return a matrix with ones in the first column and the first k lagged values of y in the other columns. I.e., for t > k, [1, y(t-1), ..., y(t-k)] is the t-th row of the result. The resulting matrix may be used as a regressor matrix in autoregressions.

@anchor{doc-bartlett}

Function File: bartlett (m)

Return the filter coefficients of a Bartlett (triangular) window of length m.

For a definition of the Bartlett window, see e.g. A. V. Oppenheim & R. W. Schafer, "Discrete-Time Signal Processing".

@anchor{doc-blackman}

Function File: blackman (m)

Return the filter coefficients of a Blackman window of length m.

For a definition of the Blackman window, see e.g. A. V. Oppenheim & R. W. Schafer, "Discrete-Time Signal Processing".

@anchor{doc-diffpara}

Function File: [d, dd] = diffpara (x, a, b)

Return the estimator d for the differencing parameter of an integrated time series.

The frequencies from @math{[2*pi*a/t, 2*pi*b/T]} are used for the estimation. If b is omitted, the interval @math{[2*pi/T, 2*pi*a/T]} is used. If both b and a are omitted then @math{a = 0.5 * sqrt (T)} and @math{b = 1.5 * sqrt (T)} is used, where @math{T} is the sample size. If x is a matrix, the differencing parameter of each column is estimated.

The estimators for all frequencies in the intervals described above is returned in dd. The value of d is simply the mean of dd.

Reference: Brockwell, Peter J. & Davis, Richard A. Time Series: Theory and Methods Springer 1987.

@anchor{doc-durbinlevinson}

Function File: durbinlevinson (c, oldphi, oldv)

Perform one step of the Durbin-Levinson algorithm.

The vector c specifies the autocovariances [gamma_0, ..., gamma_t] from lag 0 to t, oldphi specifies the coefficients based on c(t-1) and oldv specifies the corresponding error.

If oldphi and oldv are omitted, all steps from 1 to t of the algorithm are performed.

@anchor{doc-fftshift}

Function File: fftshift (v)

Perform a shift of the vector v, for use with the fft and ifft functions, in order the move the frequency 0 to the center of the vector or matrix.

If v is a vector of @math{N} elements corresponding to @math{N} time samples spaced of @math{Dt} each, then @code{fftshift (fft (v))} corresponds to frequencies

f = ((1:N) - ceil(N/2)) / N / Dt

If v is a matrix, the same holds for rows and columns.

@anchor{doc-fractdiff}

Function File: fractdiff (x, d): Compute the fractional differences @math{(1-L)^d x} where @math{L} denotes the lag-operator and @math{d} is greater than -1.

@anchor{doc-hamming}

Function File: hamming (m)

Return the filter coefficients of a Hamming window of length m.

For a definition of the Hamming window, see e.g. A. V. Oppenheim & R. W. Schafer, "Discrete-Time Signal Processing".

@anchor{doc-hanning}

Function File: hanning (m)

Return the filter coefficients of a Hanning window of length m.

For a definition of this window type, see e.g. A. V. Oppenheim & R. W. Schafer, "Discrete-Time Signal Processing".

@anchor{doc-hurst}

Function File: hurst (x): Estimate the Hurst parameter of sample x via the rescaled range statistic. If x is a matrix, the parameter is estimated for every single column.

@anchor{doc-periodogram}

Function File: periodogram (x): For a data matrix x from a sample of size n, return the periodogram.

@anchor{doc-rectangle_lw}

Function File: rectangle_lw (n, b): Rectangular lag window. Subfunction used for spectral density estimation.

@anchor{doc-rectangle_sw}

Function File: rectangle_sw (n, b): Rectangular spectral window. Subfunction used for spectral density estimation.

@anchor{doc-sinetone}

Function File: sinetone (freq, rate, sec, ampl)

Return a sinetone of frequency freq with length of sec seconds at sampling rate rate and with amplitude ampl. The arguments freq and ampl may be vectors of common size.

Defaults are rate = 8000, sec = 1 and ampl = 64.

@anchor{doc-sinewave}

Function File: sinewave (m, n, d)

Return an m-element vector with i-th element given by sin (2 * pi * (i+d-1) / n).

The default value for d is 0.

@anchor{doc-spectral_adf}

Function File: spectral_adf (c, win, b)

Return the spectral density estimator given a vector of autocovariances c, window name win, and bandwidth, b.

The window name, e.g., "triangle" or "rectangle" is used to search for a function called win_sw.

If win is omitted, the triangle window is used. If b is omitted, 1 / sqrt (length (x)) is used.

@anchor{doc-spectral_xdf}

Function File: spectral_xdf (x, win, b)

Return the spectral density estimator given a data vector x, window name win, and bandwidth, b.

The window name, e.g., "triangle" or "rectangle" is used to search for a function called win_sw.

If win is omitted, the triangle window is used. If b is omitted, 1 / sqrt (length (x)) is used.

@anchor{doc-spencer}

Function File: spencer (x): Return Spencer's 15 point moving average of every single column of x.

@anchor{doc-stft}

Function File: [y, c] = stft (x, win_size, inc, num_coef, w_type)

Compute the short-term Fourier transform of the vector x with num_coef coefficients by applying a window of win_size data points and an increment of inc points.

Before computing the Fourier transform, one of the following windows is applied:

hanning: w_type = 1
hamming: w_type = 2
rectangle: w_type = 3

The window names can be passed as strings or by the w_type number.

If not all arguments are specified, the following defaults are used: win_size = 80, inc = 24, num_coef = 64, and w_type = 1.

y = stft (x, ...) returns the absolute values of the Fourier coefficients according to the num_coef positive frequencies.

[y, c] = stft (x, ...) returns the entire STFT-matrix y and a 3-element vector c containing the window size, increment, and window type, which is needed by the synthesis function.

@anchor{doc-synthesis}

Function File: synthesis (y, c)

Compute a signal from its short-time Fourier transform y and a 3-element vector c specifying window size, increment, and window type.

The values y and c can be derived by

[y, c] = stft (x , ...)

@anchor{doc-triangle_lw}

Function File: triangle_lw (n, b): Triangular lag window. Subfunction used for spectral density estimation.

@anchor{doc-triangle_sw}

Function File: triangle_sw (n, b): Triangular spectral window. Subfunction used for spectral density estimation.

@anchor{doc-yulewalker}

Function File: [a, v] = yulewalker (c)

Fit an AR (p)-model with Yule-Walker estimates given a vector c of autocovariances [gamma_0, ..., gamma_p].

Returns the AR coefficients, a, and the variance of white noise, v.

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