Argthtjtr

I'm new to audio processing and dealing with data that's being streamed in real-time. What I want to do is:

• listen to a built-in microphone


• chunk together samples into 0.1second chunks


• convert the chunk into a periodogram via the short-time Fourier transform (STFT)


• apply some simple functions


• convert back to time series data via the inverse STFT (ISTFT)


• play back the new audio on headphones

I've been looking around for "real time spectrograms" to give me a guide on how to work with the data, but no dice. I have, however, discovered some interesting packages, including PortAudio.jl, DSP.jl and MusicProcessing.jl.

It feels like I'd need to make use of multiprocessing techniques to just store the incoming data into suitable chunks, whilst simultaneously applying some function to a previous chunk, whilst also playing another previously processed chunk. All of this feels overcomplicated, and has been putting me off from approaching this project for a while now.

Any help will be greatly appreciated, thanks.

As always start with a simple version of what you really need ... ignore for now pulling in audio from a microphone, instead write some code to synthesize a sin curve of a known frequency and use that as your input audio, or read in audio from a wav file - benefit here is its known and reproducible unlike microphone audio

this post shows how to use some of the libs you mention http://www.seaandsailor.com/audiosp_julia.html

You speak of "real time spectrogram" ... this is simply repeatedly processing a window of audio, so lets initially simplify that as well ... once you are able to read in the wav audio file then send it into a FFT call which will return back that audio curve in its frequency domain representation ... as you correctly state this freq domain data can then be sent into an inverse FFT call to give you back the original time domain audio curve

After you get above working then wrap it in a call which supplies a sliding window of audio samples to give you the "real time" benefit of being able to parse incoming audio from your microphone ... keep in mind you always use a power of 2 number of audio samples in your window of samples you feed into your FFT and IFFT calls ... lets say your window is 16384 samples ... your julia server will need to juggle multiple demands (1) pluck the next buffer of samples from your microphone feed (2) send a window of samples into your FFT and IFFT call ... be aware the number of audio samples in your sliding window will typically be wider than the size of your incoming microphone buffer - hence the notion of a sliding window ... over time add your mic buffer to the front of this window and remove same number of samples off from tail end of this window of samples