How could I generate a biphasic pulse train?

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Abir Ouji 2020-11-9

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评论： Abir Ouji 2020-11-22

hi :)

i m trying to generate a biphasic pulse train to modulate with speech signal. but all i found is pulse train with positive amplitude only.

how could I generate a biphasic pulse train ? any help ?

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Answer 1

Mathieu NOE 2020-11-9

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hello

maybe this can help you

freq = 10 ;
Ts=1e-04;
t=0:Ts:1;
angl = 2*pi*(mod(freq*t,1));
% % example 1 : positive square wave / mono phasic pulse train
% square_wav = 0.5*(sign(sin(angl))+1);
% plot(t,square_wav);
% example 2 : bi phasic pulse train
interphase_gap = 10e-3; % gap (in second)
gap_angl = 2*pi*freq*interphase_gap;
angl(angl<=gap_angl/2) = 0;
angl(abs(2*pi-angl)<=gap_angl/2) = 0;
angl(abs(pi-angl)<=gap_angl/2) = 0;
biphasic_pulse_train = sign(sin(angl));
% plot(t,angl);
plot(t,biphasic_pulse_train);

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Mathieu NOE 2020-11-20

在 MATLAB Online 中打开

hello I think I have a code that works but I have a problem with the values you gave above

if d is 33 msec (confirm) , so 33e-3 s , you cannot have 833 pulses per seconde because each pulse duration would be 66 msec, so max 15 pulses per second is doable, and it woul look like there is no time space between two consecutive pulses

so, this is the code, but the demo is made with rate = 1;

NB this is made with Fs = 11 kHz wav file

infile='DirectGuitar.wav';
% current sample is 11kHz so 0-3 ms is 0 - 33 samples
% read the sample waveform
[x,Fs] = audioread(infile);
samples = length(x);
time =(0:samples-1)/Fs;
% normalize x to +/- 1 amplitude
x = x ./ (max(abs(x)));
% square pulse wave
% rate=833 pulse/sec and d=33 msec/phase and it start's after 1/rate duration 
rate = 1; % Fs/833;
start_time = 1;     %rate/Fs; % in seconds
gap_seconds = 33e-3; % please double check
[BP_train, signal_pulsed] = bptrain(Fs,samples,rate,start_time,gap_seconds,x);
% plot
figure(1);
subplot(3,1,1),plot(time,x,'b');grid
title('input waveform');
subplot(3,1,2),plot(time,BP_train,'b');grid
title('pulse train');
subplot(3,1,3),plot(time,signal_pulsed,'b');grid
title('pulsed waveform');
function [biphasic_pulse_train, signal_pulsed]  = bptrain(Fs,samples,rate,start_time,gap_seconds,signal)
    start_sample = floor(start_time*Fs);
    gap_angl = 2*pi*rate*gap_seconds;
    sin_gap_angl = sin(gap_angl);
    
    time =(0:samples-start_sample)/Fs;
    angl = 2*pi*(mod(rate*time,1));
    
    time = (0:samples-1)/Fs;
    out = zeros(samples,1);
    angl = [zeros(1,start_sample-1) angl];
    
    ind_pos = find(angl>0 & angl<=gap_angl);
    out(ind_pos) = 1;
    
     ind_neg = find(angl>gap_angl & angl<=2*gap_angl);
    out(ind_neg) = -1;   
%    create a enveloppe of the signal before applying the product with the
%    pulse train
    N = 1000;
    envelop = myslidingavg(abs(signal), N);
    
    biphasic_pulse_train = out;
    signal_pulsed = biphasic_pulse_train.*envelop;
end
function out = myslidingavg(in, N)
%   OUTPUT_ARRAY = MYSLIDINGAVG(INPUT_ARRAY, N)
%
%  The function 'slidingavg' implements a one-dimensional filtering, applying a sliding window to a sequence. Such filtering replaces the center value in
%  the window with the average value of all the points within the window. When the sliding window is exceeding the lower or upper boundaries of the input
%  vector INPUT_ARRAY, the average is computed among the available points. Indicating with nx the length of the the input sequence, we note that for values
%  of N larger or equal to 2*(nx - 1), each value of the output data array are identical and equal to mean(in).
%
%  *  The input argument INPUT_ARRAY is the numerical data array to be processed.
%  *  The input argument N  is the number of neighboring data points to average over for each point of IN.
%
%  *  The output argument OUTPUT_ARRAY is the output data array.
if (isempty(in)) | (N<=0)                                              % If the input array is empty or N is non-positive,
 disp(sprintf('SlidingAvg: (Error) empty input data or N null.'));     % an error is reported to the standard output and the
 return;                                                               % execution of the routine is stopped.
end % if
if (N==1)                                                              % If the number of neighbouring points over which the sliding 
 out = in;                                                             % average will be performed is '1', then no average actually occur and
 return;                                                               % OUTPUT_ARRAY will be the copy of INPUT_ARRAY and the execution of the routine
end % if                                                               % is stopped.
nx   = length(in);             % The length of the input data structure is acquired to later evaluate the 'mean' over the appropriate boundaries.
if (N>=(2*(nx-1)))                                                     % If the number of neighbouring points over which the sliding 
 out = mean(in)*ones(size(in));                                        % average will be performed is large enough, then the average actually covers all the points
 return;                                                               % of INPUT_ARRAY, for each index of OUTPUT_ARRAY and some CPU time can be gained by such an approach.
end % if                                                               % The execution of the routine is stopped.
out = zeros(size(in));         % In all the other situations, the initialization of the output data structure is performed.
if rem(N,2)~=1                 % When N is even, then we proceed in taking the half of it:
 m = N/2;                      % m = N     /  2.
else                           % Otherwise (N >= 3, N odd), N-1 is even ( N-1 >= 2) and we proceed taking the half of it:
 m = (N-1)/2;                  % m = (N-1) /  2.
end % if
for i=1:nx,                                                 % For each element (i-th) contained in the input numerical array, a check must be performed:
  dist2start = i-1;         %  index distance from current index to start index (1)
  dist2end = nx-i;          %  index distance from current index to end index (nx)
  if dist2start<m || dist2end<m        % if we are close to start / end of data, reduce the mean calculation on centered data vector reduced to available samples
      dd = min(dist2start,dist2end);    % min of the two distance (start or end)
  else
      dd = m;
  end % if
  out(i) = mean(in(i-dd:i+dd));     % mean of centered data , reduced to available samples at both ends of the data vector
end % for i

Mathieu NOE 2020-11-21

在 MATLAB Online 中打开

you can of course create or download new functions (from the File Exchange for example)

there are no limits to expand matlab functionnalities

for your code the main code for you would be only this part

infile='DirectGuitar.wav';
% current sample is 11kHz so 0-3 ms is 0 - 33 samples
% read the sample waveform
[x,Fs] = audioread(infile);
samples = length(x);
time =(0:samples-1)/Fs;
% normalize x to +/- 1 amplitude
x = x ./ (max(abs(x)));
% square pulse wave
% rate=833 pulse/sec and d=33 msec/phase and it start's after 1/rate duration 
rate = 1; % Fs/833;
start_time = 1;     %rate/Fs; % in seconds
gap_seconds = 33e-3; % please double check
[BP_train, signal_pulsed] = bptrain(Fs,samples,rate,start_time,gap_seconds,x);
% plot
figure(1);
subplot(3,1,1),plot(time,x,'b');grid
title('input waveform');
subplot(3,1,2),plot(time,BP_train,'b');grid
title('pulse train');
subplot(3,1,3),plot(time,signal_pulsed,'b');grid
title('pulsed waveform');

the rest , that is the 2 subfunctions are stores are separate m functions , either in the same directory as the main code or as your own toolbox (create a folder and add it to the matlab path)

Mathieu NOE 2020-11-22

Glad it helps you

now you can relax a bit !!

good luck for the future

Abir Ouji 2020-11-22

yes I feel relieved now hh

thank you :)

you too

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