Giancarlo asked . 2021-01-29

additional findchangepts function output

I have two questions regarding function "findchangepts" (DSP Toolbox):
1. How to effectively create additional output vector "x_hat" from standard output "ipt" of the "findchangepts" function at the same sample grid as input data "x". The "x_hat" vector corresponds to function which is piecewise constant or linear approximation of the input "x" signal with jumps at detected change points "ipt" and is produced only as graphics output by "findchangepts" in case of no output variables (see internal function "cpplot" at "findchangepts.m" source code file).
2. Any idea how to choose the input parameters of the "findchangepts" function to restrict output change points only for jump steps values less than some threshold value?

matlab , signal , signal processing , findchangepts

Expert Answer

Prashant Kumar answered . 2024-12-21 00:27:44

#1 Maybe something like:
 
 
 
    function y = fitchangepts(x, icp, statistic)

    y = nan(size(x));
    K = length(icp);
    nseg = K+1;
    istart = [1; icp(:)];
    istop = [icp(:)-1; length(x)];

    if strcmp(statistic,'mean') || strcmp(statistic,'std')
      for s=1:nseg
        ix = (istart(s):istop(s))';
        y(ix) = mean(x(ix));
      end
    elseif strcmp(statistic,'rms')
      for s=1:nseg
        ix = (istart(s):istop(s))';
        y(ix) = rms(x(ix));
      end
    else % linear
      for s=1:nseg
        ix = (istart(s):istop(s))';
        y(ix) = polyval(polyfit(ix,x(ix),1),ix);
      end
    end

Test it:

load engineRPM.mat
plot(fitchangepts(x,findchangepts(x,'Statistic','linear','MinThreshold',var(x)/2),'linear'))



ipt = findchangepts(x) returns the index at which the mean of x changes most significantly.

  • If x is a vector with N elements, then findchangepts partitions x into two regions, x(1:ipt-1) and x(ipt:N), that minimize the sum of the residual (squared) error of each region from its local mean.

  • If x is an M-by-N matrix, then findchangepts partitions x into two regions, x(1:M,1:ipt-1) and x(1:M,ipt:N), returning the column index that minimizes the sum of the residual error of each region from its local M-dimensional mean.

 

Load a data file containing a recording of a train whistle sampled at 8192 Hz. Find the 10 points at which the root-mean-square level of the signal changes most significantly.

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