You could match the spectral signature to a known sample to distinguish between bubbles popping and other types of noises, if there were any, but the recording conditions seem good enough not to warrant it.
You could also possibly use an array of two, three or more mics and do some fancy triangulation to get a 3D map of the pop locations and better distinguish between individual pops, but I doubt how useful that would be besides a few cool visualizations.
I'll definitely look into applying a bandpass filter, thats a good idea!
I had wondered about the spectral signature idea, I was wondering if I could simply slide a window across the data, applying some kind of correlation function, using the magnitude data from the FFT of a known 'bubble' output compared to the window. But I wasn't sure what kind of correlation function to use, whether maybe Pearson correlation coefficient would be sensible?
I like your idea of using multiple microphones to get a 3D visualisation.
Do you think my sample rate is high enough for acoustic emission detection, as I notice in your other comment you mention they often use sample rates many times higher.
Also is there a particular paper you'd recommend regarding acoustic emission detection?
48khz is not overkill. Acoustic emission sensors used to detect signals like this typically use many times that sampling rate.
Or is the sensor in the fermenter itself and you're kind of measuring the pressure?
This made me smile. Did you take continuous measurements then? You could do a windowed FFT and just take snapshots.
I took the audio recordings first, then processed the .wav files later via FFT. That's a good point though, but having the raw audio does let me play around with different techniques at a later date.
I will have to look into a sliding window, if that's what you mean though, because at the moment I'm simply looking at each chunk of N seconds of audio.
When I've tweaked the approach it would definitely be better to switch to the type of system you mention.