Detect Greatest Dynamic Range Changes In Audio Sample - algorithm

Question

Forgive/correct any wrong terminology in the below (I hope it makes sense!):
I want to detect the biggest dynamic audio changes in a given sample, (ie. the moments when the sound wave 'grows'/'accelerates' the most).
For example, if the audio goes quiet at some points during the sample, I want to know when the music comes back in after, and order these data points by the relative dynamic range (volume?) increase (largest to smallest).
My audio sample is a buffer of float32[] and sample rate, and I would like a resulting array of objects each containing:
start frame index
start time (seconds ... frameIndex/sampleRate?)
end frame index
end time (seconds)
dynamic change value
My naive approach iterates linearly and detects points at which the value starts rising until it is no longer rising, and then calculates the rise over run for each sub interval between those points.. but this is not producing the correct result.
Any ideas or existing algorithms that do this?
Not picky on languages, but anything with syntax like C#, Java, JavaScript preferred!

Answer 1

I am a little unsure as to how much audio DSP background you have so apologies if treading over old territory.
Essentially this is a problem of trying to find the envelope of the signal at any given point.
Since the audio signal will be fluctuating between -1 and 1, the value of any individual sample will not yield much
information about the loudness or dynamic range.
What would be best to find is the root mean square of the signal over some frame of audio data
Written in pseudo code, and assuming you already have your audio Data, a function and way of grabbing the rms data could be:
function rms(frame[], frameSize)
{
var rmsValue = 0;
for(int i = 0; i < frameSize; i++)
{
rmsValue += frame[i] * frame[i]; // square the sample and sum over frame
}
rmsValue = sqrt(rmsValue / frameSize);
return rmsValue;
}
// Main
var frameNum = floor(numberOfAudioSample / frameSize) // for analysis just floor to a whole number of frames, if thi is real-time, you will need to deal with a partial frame at the end
var frame = [] // an array or buffer to temporarily store audio data
var rmsData = [] // an array or buffer to store RMS data
for (var i = 0; i < frameNum; i++)
{
for (var j = 0; j < frameSize; j++)
{
sampleIndex = j + (i * frameSize)
frame[j] = audioData[sampleIndex]
}
rmsData[i] = rms(frame, frameSize)
}
You can then compare elements of the RMS Data to find when the dynamics are changing and by how much.
For digital audio RMS will be constrained to between 0 and 1. To get dBFS then all you need to do is 20 * log10(rmsData)
Finding the exact sample where dynamic range changes will be tricky. The frame index should be accurate enough with a small enough size of frame.
The smaller the frame, however, the more erratic the RMS values will be. Finding a time in seconds is simply sampleIndex / samplingRate
With a small frame size you may also want to low pass filter the rms data. It depends on whether this is for a real-time application or for non real-time analysis.
To make things easy, I would prototype something in Octave or MATLAB first