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SuperCollider – A platform for audio synthesis and algorithmic composition

supercollider.github.io

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Re: SuperCollider – A platform for audio synthesis and algorithmic composition

#11

Post by nanomonkey »

AndrewUnmuted wrote: post #4
nanomonkey wrote: post #2

For Clojure fans, there is [Overtone]( https://overtone.github.io/ ), which uses the SuperCollider audio engine, but allows you to write more functional code and utilize REPL based development (within emacs!).

The SC3 syntax was designed with real-time audio workflows in mind. Though the language extensions for SuperCollider are nice, there’s real benefit to learning the actual language as well.

No knock on the original Super Collider language, sclang. That is what I originally learned and used.

For someone like me, who doesn't have much time to context switch when playing around with making music, sticking with Clojure, which I've settled on as my language of choice, is a no brainer.

Re: SuperCollider – A platform for audio synthesis and algorithmic composition

#13

Post by n3k5 »

amelius wrote: post #10
n3k5 wrote: post #9

Earlier quoted context omitted.

Interesting thought. SciPy is obviously useful for modelling DSP stuff, but no idea how feasible it would be to use it for real-time processing, compared to something like Pyo ( http://ajaxsoundstudio.com/pyodoc/ ). I just tried searching "Jupyter audio live coding" for fun and it unearthed quite a few interesting results, but the real-time ones tend to involve SuperCollider or something similar. E.g. I discovered NS…

I suppose that many operations in DSP are just linear transforms. So you could pre-compute transforms using SciPy (and its wealth of available functions). And then you could have a pipelined version of the BLAS matrix-vector multiplication to make it low-latency. Possibly that could run on the GPU.

Yup, making it fast is no problem; I was just concerned about achieving low latency without dropping samples. The typical cookbook examples usually output to matplotlib or maybe an audio file, rather than straight to DAC, and I had amplified such search results by specifically looking for Jupyter examples (and also missed a lot of work that was done when it was still called IPython).

Upon digging a bit deeper it turns out using vanilla SciPy for real-time DSP is totally a thing.

Live-coding adds some additional demands regarding a different kind of latency — pre-computations need to happen as quickly as possible — but it seems feasible.

Re: SuperCollider – A platform for audio synthesis and algorithmic composition

#14

Post by amelius »

n3k5 wrote: post #13
amelius wrote: post #10

Earlier quoted context omitted.

I suppose that many operations in DSP are just linear transforms. So you could pre-compute transforms using SciPy (and its wealth of available functions). And then you could have a pipelined version of the BLAS matrix-vector multiplication to make it low-latency. Possibly that could run on the GPU.

Yup, making it fast is no problem; I was just concerned about achieving low latency without dropping samples. The typical cookbook examples usually output to matplotlib or maybe an audio file, rather than straight to DAC, and I had amplified such search results by specifically looking for Jupyter examples (and also missed a lot of work that was done when it was still called IPython). Upon digging a bit deeper it turn…

Interesting! Could you share some of the links that you came across in your exploration?

Re: SuperCollider – A platform for audio synthesis and algorithmic composition

#15

Post by n3k5 »

amelius wrote: post #14
n3k5 wrote: post #13

Earlier quoted context omitted.

Yup, making it fast is no problem; I was just concerned about achieving low latency without dropping samples. The typical cookbook examples usually output to matplotlib or maybe an audio file, rather than straight to DAC, and I had amplified such search results by specifically looking for Jupyter examples (and also missed a lot of work that was done when it was still called IPython). Upon digging a bit deeper it turn…

Interesting! Could you share some of the links that you came across in your exploration?

Basically I just skimmed through https://www.google.com/search?q=scipy+dsp+real-time and weeded out false positives that do use SciPy in some capacity, but also more specialised stuff such as the aforementioned Pyo. This mostly got rid of the music-related stuff (à la ‘what if guitar effect but Python instead of sclang’) and left me with e.g. Stack Overflow posts that simply confirm ‘yes, it's feasible’.

But I have two noteworthy links:

https://warrenweckesser.github.io/papers/weckesser-scipy-lin...

Particularly “Filtering a long signal in batches” on page 6, where it shows how to apply the Butterworth filter from the previous section to individual windows while preserving its state across invocations. As I'm familiar with NumPy, but very ignorant about scipy.signal, this was the ‘Bingo!’ moment for me :)

https://scikit-dsp-comm.readthedocs.io/en/latest/

> This allows in particular demodulation of radio signals and downsampling to baseband analog signals for streaming playback of say an FM broadcast station.

I didn't dig into where it does the heavy lifting for that (sample rates in the MHz range) — there may be some C/C++ involved. But the docs show some nice examples of how to do streaming audio DSP with NumPy, SciPy and PyAudio inside Jupyter:

https://scikit-dsp-comm.readthedocs.io/en/latest/nb_examples...

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