How to vectorise the filter function with multiple window-sizes?

The task is to calculate the maximum of a moving rms-value of a signal, which is available with a constant timestep dt. So the signal has the length of T*dt and the RMS-value shall be calculated for a period tau=1*dt, tau=2*dt, tau=3*dt, ... tau=T*dt. At the end of this calulation the max of the moving rms value shall be selected. Actually I use the filter-function in a for-loop, but maybe it is possible to vectorise the calculation?

The code is below:

function [tau, werte_eff_H ] = twa_stat_2(signal)
% any signal-vector;
signal_eff  =   signal.^2;                           % To calculate the RMS-Values 
no_value    =   size(signal,1); 
tau         =   (1:1:no_value)';
werte_eff_H =   NaN(size(signal,1)+1, no_value);     % Preallocation
for i=1:size(tau,1)
    zb_werte_eff        =   sqrt(filter(ones(1,tau(i,1))./tau(i,1),1,[signal_eff(1:no_value); signal_eff(1:tau(i,1)-1,1); 0 ] ));  
    werte_eff_H(:,i)    =   zb_werte_eff(0+tau(i,1):no_value+tau(i,1),1);                                                          
end
werte_eff_H  = werte_eff_H(1:end-1,:);  
end

[Jan]

What is a typical length of the signal?

Answer 1

[Jan]

You can save 25% runtime by calculating the srqt() only of the used values and create the padded signal once only - and some slightly changes:

function [tau, werte_eff_H ] = twa_stat_2(signal)
% any signal-vector;
signal_eff  = signal.^2;                           % To calculate the RMS-Values 
no_value    = size(signal,1); 
tau         = (1:1:no_value)';
werte_eff_H = zeros(size(signal,1), no_value);     % Preallocation
signal2     = [signal_eff; signal_eff];
for i=1:no_value
    zb_werte_eff     = filter(ones(1,i) ./ i, 1, signal2(1:no_value + i,1));
    werte_eff_H(:,i) = zb_werte_eff(i:no_value+i-1,1);
end
werte_eff_H = sqrt(werte_eff_H);  
end

A vectorization of the different filter calls is not possible, as far as I can see. Using parfor might be useful, but filter is multithreaded already and if all cores are used, a parallelization will not have a positive effect.

Perhaps the movmean filter is faster than filter. Do you have a C-compiler? Then a hand made C-Mex functions for the moving average will be faster.

Answer 2

[dalya anewr]

hi text2speech Error using feval Input PROGID does not represent an Activex control. If this PROGID used to work before, please check vendor's documentation for equivalent activex control progid.

Error in actxcontrol

Error in actxcontrol

Error in text2speech (line 55) vlc1 = actxcontrol('VideoLAN.VLCPlugin.2', [1 1 0 0], fig); please i need help

[Jan]

Please do not highjack another thread. This is not an answer to the original question. Open a new thread and delete this message. Thanks.