I would like to generate accurate peak and RMS information from an AudioNode. Given the amount of smartness in Tone.js to avoid script processors whenever possible, I am surprised that their volume meter does use a script processor to calculate RMS volume.
I am thinking that it must be possible to translate the following SuperCollider code that I have used for years for reliable metering:
val sig = In.ar("bus".ir, bus.numChannels)
val tr = Impulse.kr(20)
val peak = Peak.kr(sig, tr)
val rms = A2K.kr(Lag.ar(sig.squared, 0.1))
SendReply.kr(tr, Flatten(Zip(peak, rms)), "/$meter")
That is I need a resetable running-max and a smoothing filter for squared input. The problem is that with the limited existing modules in Web Audio API, I would probably need something like a single-sample-delay feedback loop which doesn't exist.
Perhaps the RMS I can realise with a Gain and BiQuad stage? Any thoughts (also in terms of performance)? Any ideas how to get the running max (without script processor)?
You can get an approximate RMS with a biquad, but you can't get running max without a script processor.
Related
I am using flutter_sound package to record audio from mic. It provides data in stream of Uint8List. So how can I calculate amplitude from it. I have found many answers in other language but I was having hard time interpreting it into dart.
for reference,
Reading in a WAV file and calculating RMS
Detect silence when recording
how can i translate byte[] buffer to amplitude level
if anyone can interpret this into dart so that I can calculate amplitude
I was not able to calculate amplitude from Uint8List so what I did was write my own native code to directly get amplitude from native you can check out my package here. (I am busy somewhere else so I'm not right now maintaining it but will surely do in future.)
And I don't know but flutter_sound provides maybe false values or I was calculating wrong but I was always getting nearly the same values even when the volume is high or low.
Now, if you want to calculate amplitude then it should be based on values you're getting,
If you are getting values between -32768 and 32767 then from my understanding what you do is define a timeframe and for that timeframe calculate the RMS of it.
The RMS is your amplitude.
Now, as #richard said in the comment to normalize with 32768 if you have Int16List. But I guess that is doing too much work. We get a stream of Int16List so data may be coming every 200ms or whatever it is set at the plugin level. Calculating RMS for each Int16List is a very intensive task so I don't think it's good to do it on the flutter side. Get data from native directly and just pass it flutter using platform channels.
Now, if you want to try then calculate the RMS of an Int16List then for a defined timeframe take the mean of those values.
I may well be wrong but hope this helps or guides you in the right direction. If anyone finds a better way please post it here.
I would like to get peak value from STM32 adc samples. I have written the below code and I've managed to get peak value however most of the time this value includes the biggest noise. In order to eliminate noise effects, I have decided to apply averaging method. I would like to get 5 measurements' averages. Then I'd like to compare these averages and use the biggest one(biggest average). Can anybody suggest a code?
Regards,
Umut
void HAL_ADC_ConvCpltCallback(ADC_HandleTypeDef* hadc)
{
ADC_raw = HAL_ADC_GetValue(hadc);
Vdd = 3.3 * (ADC_raw)/4095;
if (Vdd > Vmax)
{
Vmax = Vdd;
}
At first, I would remove as much code as possible from the Callback function because it is still inside the interrupt context which should be as short as possible. This is mentioned in a lot of answears here so I will not go into details on how to handle this.
For averaging the measurement, there are multiple ways you can go.
Automatic avarage
Use the ADCs oversampling function. The controller will sample the signal multiple times (configure using OVFS register) and calculate an average value before triggering the interrupt.
Manual average
Using the HAL_ADC_ConvCpltCallback function Store the numer of desired value into an array and calculate the average in the main loop.
Manual average using DMA
Let the DMA store the number of samples you want to use in an array using the function HAL_ADC_Start_DMA. When all samples have been collected you will be notified. This will reduce the processor load because you don't have to shift the data into the array yourself.
You can also combine the oversampling (most of the time a good idea) and one of the other methods depending on your Use-Case.
I have some data which is time-stamped by a NMEA GPS string that I decode in order to obtain the single data point Year, Month, Day, etcetera.
The problem is is that in few occasions the GPS (probably due to some signal loss) goes boinks and it spits out very very wrong stuff. This generates spikes in the time-stamp data as you can see from the attached picture which plots the vector of Days as outputted by the GPS.
As you can see, the GPS data are generally well behaved, and the days go between 1 and 30/31 each month before falling back to 1 at the next month. In certain moments though, the GPS spits out a random day.
I tried all the standard MATLAB functions for despiking (such as medfilt1 and findpeaks), but either they are not suited to the task, either I do not know how to set them up properly.
My other idea was to loop over differences between adjacent elements, but the vector is so big that the computer cannot really handle it.
Is there any vectorized way to go down such a road and detect those spikes?
Thanks so much!
you need to filter your data using a simple low pass to get rid of the outliers:
windowSize = 5;
b = (1/windowSize)*ones(1,windowSize);
a = 1;
FILTERED_DATA = filter(b,a,YOUR_DATA);
just play a bit with the windowSize until you get the smoothness you want.
In the course of testing an algorithm I computed option prices for random input values using the standard pricing function blsprice implemented in MATLAB's Financial Toolbox.
Surprisingly ( at least for me ) ,
the function seems to return negative option prices for certain combinations of input values.
As an example take the following:
> [Call,Put]=blsprice(67.6201,170.3190,0.0129,0.80,0.1277)
Call =-7.2942e-15
Put = 100.9502
If I change time to expiration to 0.79 or 0.81, the value becomes non-negative as I would expect.
Did anyone of you ever experience something similar and can come up with a short explanation why that happens?
I don't know which version of the Financial Toolbox you are using but for me (TB 2007b) it works fine.
When running:
[Call,Put]=blsprice(67.6201,170.3190,0.0129,0.80,0.1277)
I get the following:
Call = 9.3930e-016
Put = 100.9502
Which is indeed positive
Bit late but I have come across things like this before. The small negative value can be attributed to numerical rounding error and / or truncation error within the routine used to compute the cumulative normal distribution.
As you know computers are not perfect and small numerical error always persists in all calculations, in my view therefore the question one should must ask instead is - what is the accuracy of the input parameters being used and therefore what is the error tolerance for outputs.
The way I thought about it when I encountered it before was that, in finance, typical annual stock price return variance are of the order of 30% which means the mean returns are typically sampled with standard error of roughly 30% / sqrt(N) which is roughly of the order of +/- 1% assuming 2 years worth of data (so N = 260 x 2 = 520, any more data you have the other problem of stationarity assumption). Therefore on that basis the answer you got above could have been interpreted as zero given the error tolerance.
Also we typically work to penny / cent accuracy and again on that basis the answer you had could be interpreted as zero.
Just thought I'd give my 2c hope this is helpful in some ways if you are still checking for answers!
I am trying to make a fatigue analysis from a load series and I woudl like to extract the equivalent moment for
number cycles=1000000
years=25
I have a time series for one hour that looks like:
Then I have read that Rainflow Analysis is a very good tool to extract the cycles from a time history. Therefore I apply:
%Rainflow moment
dt=time(2)-time(1);
[timeSeriesSig, extt] = sig2ext(timeSeries, dt);
rf = rainflow(timeSeriesSig,extt);
I read that
OUTPUT
rf - rainflow cycles: matrix 3xn or 5xn dependend on input,
rf(1,:) Cycles amplitude,
rf(2,:) Cycles mean value,
rf(3,:) Number of cycles (0.5 or 1.0),
rf(4,:) Begining time (when input includes dt or extt data),
rf(5,:) Cycle period (when input includes dt or extt data),
If I am interested in the number of cycles what does the term rf(3,:) mean? It is only containing 0.5 and 1 in the vector. I want to obtain an histogram with the number of cycles per bin amplitude. Thanks
If you are using code from the File Exchange or another source, it helps to link to where you obtained it.
Here, rf(3,:) is showing you whether the amplitudes and other outputs refer to half or full cycles. The rainflow algorithm finds half-cycles first, and then matches tensile and compressive half-cycles of equal amplitude to find full cycles. There are normally some residual half-cycles.
If you examine the rfhist function contained within that same File Exchange submission, you will see how they handle full and half amplitudes. Basically, two half-cycles at any given load amplitude will be counted as one full cycle when creating the histogram.