torvalds/AudioNoisePublic

Random digital audio effects

AI summary: Linus Torvalds's educational C sandbox for simulating digital audio effects using basic DSP techniques.

Stars
4.5K
+6 today
Forks
224
Watchers
58
Open issues
19
Open PRs
15
Contributors
~3
Commits
47
Branches
1

CGPL-2.0Created Jan 9, 2026Last push 4mo ago+10 stars this week+30 this month

Quick answers

What is AudioNoise?
Linus Torvalds's educational C sandbox for simulating digital audio effects using basic DSP techniques.
What does AudioNoise do?
AudioNoise is an experimental side project by Linus Torvalds dedicated to exploring digital audio signal processing (DSP), building upon his earlier work with analog guitar pedals. Instead of interfacing with physical microcontrollers, this repository focuses purely on simulating digital audio effects in software using a strict single-sample-in, single-sample-out paradigm. It implements fundamental techniques like infinite impulse response (IIR) filters and basic delay loops to emulate toy audio effects, such as phasers via digital all-pass filters. It serves primarily as an educational sandbox to learn audio manipulation without the overhead of complex DAW frameworks.
Who is AudioNoise for?
Developers interested in digital signal processing, audio programming in C, and following Linus Torvalds's educational side projects.
How do I get started with AudioNoise?
make
How popular is AudioNoise on GitHub?
torvalds/AudioNoise has 4,514 stars and 224 forks on GitHub, and gained 10 stars in the last 7 days.
What license does AudioNoise use?
torvalds/AudioNoise is released under the GPL-2.0 license.

Star history

since Jul 29, 2026
02K4KJul 2026Aug 2026Sep 2026Oct 2026
4.5K stars as of Oct 3, 2026. Measured daily since Jul 29, 2026; GitHub no longer exposes earlier star timestamps.

Contribution activity

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46 commits in the last yearLessMore

Signals and awards

derived from tracked data
  • Continuous integration

    Automated checks passing

  • Repeat trending

    4 trending appearances

What AudioNoise does

AudioNoise is an experimental side project by Linus Torvalds dedicated to exploring digital audio signal processing (DSP), building upon his earlier work with analog guitar pedals. Instead of interfacing with physical microcontrollers, this repository focuses purely on simulating digital audio effects in software using a strict single-sample-in, single-sample-out paradigm. It implements fundamental techniques like infinite impulse response (IIR) filters and basic delay loops to emulate toy audio effects, such as phasers via digital all-pass filters. It serves primarily as an educational sandbox to learn audio manipulation without the overhead of complex DAW frameworks.

Developers interested in digital signal processing, audio programming in C, and following Linus Torvalds's educational side projects.

  • Pure software simulation: Explores digital audio effects entirely in C software without requiring specific RP2354 hardware.
  • Fundamental DSP implementation: Builds basic techniques like IIR filters and delay loops from scratch without relying on external audio libraries.
  • Zero-latency architecture: Enforces a strict single-sample-in, single-sample-out processing model to emulate real-time hardware behavior.
  • Toy effect emulation: Focuses on simulating simple analog-style effects like phasers rather than building production-grade digital algorithms.
  • Python visualizer integration: Includes a basic audio sample visualizer tool to help verify and debug filter outputs visually.

Where teams use it

DSP Education

Developers study the C source code to understand how basic digital audio filters are mathematically implemented from scratch.

Algorithm Prototyping

Audio engineers prototype and refine simple digital guitar effects in software before porting them to dedicated microcontroller hardware.

Audio Programming Fundamentals

Students learn how to manipulate audio streams and apply basic effects without the abstraction of complex frameworks like JUCE.

Filter Visualization

Programmers test coupled LFO systems and visualize the resulting waveforms using the provided Python analysis tools.

Getting started: make

README

main branch

Another silly guitar-pedal-related repo

The digital RP2354 and TAC5112-based guitar pedal actually does work, even if I'm not thrilled about some of my analog interface choices (ie the pots in particular, and I'm growing to hate the clicky footswitch even if I do love how it also doubles as a boot selector switch for programming).

But while the hardware design is archived while I ponder the mysteries of life and physical user interfaces, I'm still looking at the digital effects on the side. But right now purely in a "since it's all digital, let's simulate it and not worry about the hardware so much".

These are -- like the analog circuits that started my journey -- toy effects that you shouldn't take seriously. The main design goal has been to learn about digital audio processing basics. Exactly like the guitar pedal was about learning about the hardware side.

So no fancy FFT-based vocoders or anything like that, just IIR filters and basic delay loops. Everything is "single sample in, single sample out with no latency". The sample may be stored in a delay loop to be looked up later (for eacho effects), but it's not doing any real processing.

I was happy with how the TAC5112 had sub-ms latencies for feeding through the ADC->DAC chain, and this is meant to continue exactly that kind of thing. Plus it's not like I've done any of this before, so it's all very basic and simple just by virtue of me being a newbie.

Put another way: the IIR filters aren't the fancy AI "emulate a cab" kind of a modern pedal or guitar amp. No, while they do emulate analog circuits like a phaser, they do so by emulating the effects of a RC network with just a digital all-pass filter, not by doing anything actually clever.

Also note that the python visualizer tool has been basically written by vibe-coding. I know more about analog filters -- and that's not saying much -- than I do about python. It started out as my typical "google and do the monkey-see-monkey-do" kind of programming, but then I cut out the middle-man -- me -- and just used Google Antigravity to do the audio sample visualizer.

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Code frequency

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46 commits in the last 52 weeks.

When work happens

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Commit volume by weekday and hour (UTC). Larger dots mean more commits.

Who is committing

last 52 weeks
Maintainer commits45 (96%)
Community commits2 (4%)

47 commits in total over the last year.

DateListRankStars gained
Jan 14, 2026daily#16+178
Jan 13, 2026daily#9+270
Jan 12, 2026daily#4+362
Jan 11, 2026daily#2+424
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