Fast time integration troubles

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Federico
Federico 2023-5-10
评论： William Rose 2023-5-16
% Importar datos de Excel
[data, ~] = xlsread('Pruebawavwav.xlsx', 'Pruebawav');
% Voltaje de referencia del ADC en V
Vref = 3.3;
% Ganancia del sensor
gain = 25;
% Convertir valores del ADC a voltajes
data_volts = (data / 1024) * Vref;
% Ajustar voltajes por la ganancia del sensor
data_volts_gain = data_volts / gain;
% Frecuencia de muestreo en Hz
fs = 44100;
% Coeficientes de la ponderación A según la norma ANSI S1.42
A_weighting_coeffs = [1.2589, -2.0082, 0.8315];
% Crear filtro de ponderación A con los coeficientes de la norma ANSI S1.42
A_weighting_filter = dsp.FIRFilter('Numerator', A_weighting_coeffs);
% Filtrar datos
data_filtered = A_weighting_filter(data_volts_gain);
% Coeficiente de suavizado del filtro de media móvil exponencial
alpha = 0.1;
% Coeficientes del filtro de media móvil exponencial
b = [alpha];
% Aplicar filtro de media móvil exponencial
data_fast_avg = filter(b, 1, data_filtered);
% Definir la presión sonora de referencia en Pascals
pref = 20e-6;
% Calcular el nivel de presión sonora en dB utilizando los datos filtrados en modo Fast
data_dB = 20 * log10(abs(data_fast_avg) / pref);
% Graficar señal filtrada en modo Fast
figure;
plot(data_dB);
title('Señal Filtrada Ponderación A (Modo Fast)');
xlabel('Ventanas de 125 ms');
ylabel('Nivel de Presión Sonora (dB)');
Im currently working in a project where itake data from a max4466 through a wemos d1 mini, an export it to an excel. After i upload the excel in matlab, the aim is that the code must work as a sound pressure level. For this i have to use an A weighting filte and a FAST time integration.
If someone knows about this topic, i would like if i can get some feedback about my code.
Thanks
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William Rose
William Rose 2023-5-12
@Federico, I think your code for fast integration is not doing what you want. What you want is to smooth the signal with an exponential time constant of 125 msec. I also doubt that your FIR filter is acheiving the A weighting standard, but I am less certain about that. I also have doubts about the calibration to Pascals and then to dB(SPL). See details in my answer below. I agree with @VBBV that it would help if you posted Pruebawavwav.xlsx, or a fragment of it.
Federico
Federico 2023-5-12
Hi @William Rose, in my last response to@VBBV i attached the file, please let me know if you can open it.
On the other hand, i looked at your last answer and will be anwsering it asap.

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回答（1 个）

William Rose
William Rose 2023-5-12
@Federico,
The code you use to convert data to data_volts looks reasonable. Your code assumes that the ADC produces an output of 1024 at 3.3 volts and 0 at 0 volts. (A 10 bit ADC has a maximum numeric output=1023, but that differentce is negligible.) Then you divide data_volts by by gain to get data_volts_gain, which is the voltage before amplification. That looks reasonable to me.
Then you filter the data, by creating a FIR filter and applying it to data_volts_gain. I am surprised that an FIR filter with just 3 coefficients can effectively implement an A-weighting filter. If I were doing it, I would check that part, and I would compare the FIR filter results to results obtained with an IIR implementaiton of the A weighting standard. Here is a paper on getting the coefficients for an IIR version of the A weighting filter. Here is another paper. In your comments, you refer to ANSI standard S 1.42. The ANSI standards are very expensive to obtain, so I have not read it. Here is a site that summarizes the A weighting equations. It also refers to a IIR filter implemnation of the A standard in Matlab. Here is the older ANSI 1.4 standard, which does not cost anything, which describes the A weighting.
Then you create an IIR filter to do the Fast averaging. Fast averaging is an exponential decay filter with a 125 millisecond time constant. The Fast avergaging standard is defined in the IEC 61672-1 standard. This standard, like the ANSI standards, is very expensive, so I have not read it. It is not clear to me whether the exponential decay filter should be applied to the signal measured in Pascals (or its voltage equivalent), or to the sound pressure after it has been converted to dB, or to the intensity (Pascals squared). This site includes an image with Pascals squared on the vertical axis, when describing the Fast and Slow filters. If that is correct, the you should covert the signal to Pascals, then square it, then apply the Fast exponential decay filter. Your current code does not do this.
The exponential decay filter which you use to do the fast averaging does not look right to me. A discrete time version of an exponential decay filter, with time constant tau, is
fs=44100; % sampling rate (Hz)
dt=1/fs; % sampling interval (s)
tau=0.125; % time constant for Fast filter, IEC 61672-1 (s)
b=dt/(tau+dt); % numerator coefficients for IIR filter
a=[1,-tau/(tau+dt)]; % denominator coefficients for IIR filter
y=filter(b,a,x); % apply filter to signal x
Your values for b and a are very different, and I think they will not give the desired result. I have used tau=0.125 s above, since that is the value for tau specified by IEC 61672-1, according to various online sources.
After doing the A-weight filtering and the fast filtering, you convert the signal to dB. I think you are trying to convert the signal to dB(SPL) rather than dBV or dBm. If I am correct, then the equation you use does not appear to make sense to me. You do
pref = 20e-6; % Definir la presión sonora de referencia en Pascals
data_dB = 20 * log10(abs(data_fast_avg) / pref);
The code above would make sense if the signal in data_fast_avg already has units of Pascals, because 20e-6 Pascals corresponds to 0 dB SPL. But I am not confident that data_fast_avg does have those units. I recommend that you confirm the calibration factors.
If the signal has units of Pa, and then you square it, before doing fast averaging, which would be consistent with the plot here, then the output from fast averaging will have units of Pa^2. Suppose the output from fast averaging is dataFast , and suppose it has units of Pa^2. Then, to convert to dB SPL, you would do
pref=20e-6; % pressure in Pa that corresponds to 0 dB SPL
data_dBSPL = 10*log10(dataFast/pref^2); % convert signal from Pa^2 to dB SPL.
Note that I squared pref in the denominator, and I multipled by 10, not 20.
Good luck.
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William Rose
William Rose 2023-5-16
@Federico,
You wrote:
I would like to know if it is a way to convert this code into an arduino compatible one. In a future i would like to be able to measure in real time. The tricky part is the .ino code needs to be able to run in a Wemos D1 Mini.
I am not able to assist with converting the code to run on an Arduino.
You wrote:
I still dont get why the data_dB equation its like this:
data_dB = 10 * log10(data_fast_avg / pref^2);
instead of:
data_dB = 20 * log10(data_fast_avg / pref^2);
Note that data_fast_avg, in my code, is filtered version of the squared pressure.
The equation for SPL in dB is
where p is the root mean square pressure in Pa, and .
Due to the mathematics of logarithms, this is equivalent to
and data_fast_avg is the smothed, or averaged, versino of the squared pressure, which means data_fast_avg is analogous to mean square pressure.
William Rose
William Rose 2023-5-16
@Federico,
You wrote:
I do have the Audio Toolbox. My doubt is if im using the Excel data, would it be compatible to the weightingFilter() command?
I am glad to hear you have the Audio Toolbox. You can use data imported from Excel with the weightingFilter() funciton. Once you have imported the data, it doesn't matter whether it came from a .txt file or an Excel file or a .csv file or some other file type.
I'm sorry if I misunderstood your question.

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