>
>  http://www.dsprelated.com/showmessage/69319/1.php
>-- 

Unfortunately, I have spent the better part of two weeks trying to
understand how to turn my unweighted RMS measurements into A-Weighted
measurements.  I've been good at finding what A-Weighting is and have found
formulas for conversion but it is more complex than applying a formula.  I
haven't found anything that explains how you can get from here to there. 
The closest thing I have found was too complex in terms of mathematics.  I
found an article that I was able to glean bits of information to improve my
comprehension of how to get to where I need to go.
http://web.media.mit.edu/~dlanman/courses/decibel_meter.pdf 

I don't use MatLab and this was more than I could handle but I *think* I
got that using FFT was ideal to the extent that it separated frequencies
needed to give to the A-Weighted filter formula for computation.  I also
got that it is more computationally expensive to use FFT than to use
filters.  What kind of filters would you use.  I am assuming that if you
are using multiple filters, that you must combine the results from the
filters in some manner. 

I am lacking knowledge but I haven't given up.  I'm hoping for some
revelation that will get me on the right track soon.

Thank you,


W.

"WaverlyE" <waverly.edwards@n_o_s_p_a_m.genesys.com> writes:

> Hello, I am attempting to reproduce the results of a psychological
> experiment.  In the original experiments, messages were delivered -15 to 25
> db(A) relative to ambient environment or white noise source using
> loudspeakers.  In my experiments I wish to deliver messages via headphones
> -15 to 25 db unweighted (RMS average), in an unweighted medium such as
> white noise.  If I keep the same level of -15 to 25 db, essentially keeping
> the same ratio, should I get the same effect?  

First, you seem to be confusing signal level with signal-to-noise
ratio (SNR). "dBA" is a measure of absolute power and says nothing
about SNR.

But to answer your question about dBA, it depends on the spectrum of the
input signal. The A weighting curve is a filter that deemphasizes low
frequencies, down 10 dB at 100 Hz - see

  http://en.wikipedia.org/wiki/A-weighting

So if the input signal contains significant low-frequency content, the
unweighted power will be much higher than the weighted power.

> My desire is not to weight the audio stream to keep the amplification
> calculations simple.

Then your results will likely not be accurate.

> If not, are there algorithms that I may manipulate my digital audio to be
> within that weighted range. 

That same article has the s-domain transfer functions for each of the
weightings, but that doesn't do you much good with digital
data. Converting these to digital filters is not a straight-forward
task. 

There was some discussion about it here a few years back that may
help you:

  http://www.dsprelated.com/showmessage/69319/1.php
-- 
Randy Yates                      % "How's life on earth? 
Digital Signal Labs              %  ... What is it worth?" 
mailto://yates@ieee.org          % 'Mission (A World Record)', 
http://www.digitalsignallabs.com % *A New World Record*, ELO

Hello, I am attempting to reproduce the results of a psychological
experiment.  In the original experiments, messages were delivered -15 to 25
db(A) relative to ambient environment or white noise source using
loudspeakers.  In my experiments I wish to deliver messages via headphones
-15 to 25 db unweighted (RMS average), in an unweighted medium such as
white noise.  If I keep the same level of -15 to 25 db, essentially keeping
the same ratio, should I get the same effect?  My desire is not to weight
the audio stream to keep the amplification calculations simple.

If not, are there algorithms that I may manipulate my digital audio to be
within that weighted range. Currently I am analyzing the RMS, less silence,
to determine what the audio’s unweighted average.  I am using 50ms
windows and anything below 44db is considered silence and is not used to
factor the RMS.



Thank you,


Waverly