[yupper,ylower] = envelope(x,wl,'rms') returns the upper and lower root-mean-square envelopes of x. The envelopes are determined using a sliding window of length wl samples.

from MATLAB doc.

Hello Kwaku ,

Let xData be the corresponding signal vector comprising the 13,230,000 data points. You can execute the following Matlab code :

>> Nbloc = floor(length(xData), 441000); % number of blocs of 441000 samples

>> Lsize = Nbloc * 441000;

>> xMatrix = reshape(xData(1:Lsize) ,441000, Nbloc); %reshape of xData vector into a matrix with size 44100 X Nbloc

>> xRMS = rms( xMatrix); % rms levels of the columns of xMatrix

In the vector xRMS you have got the RMS value computed on each bloc of 441000 successive samples corresponding to a 10 seconds window.

Sincerely,

Pascal.

As 1st stage of digital processing of such signal you can use the decimation procedure with accumulation an example 512, 1024, or more samples. I used this by processing radars and communications signals:

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