What is audionoise?

torvalds/audionoise — explained in plain English

Analysis updated 2026-06-26

★ 4,374CAudience · developerComplexity · 3/5Setup · moderate

Mindmap

mindmap
  root((audionoise))
    What it does
      Guitar audio effects
      IIR filter phaser
      Delay loop effects
    Tech
      C language
      Python visualizer
      DSP concepts
    Design
      Sample by sample
      Low latency
      Hardware companion
    Context
      Personal learning project
      Linus Torvalds
      Not production use

mindmap root((audionoise)) What it does Guitar audio effects IIR filter phaser Delay loop effects Tech C language Python visualizer DSP concepts Design Sample by sample Low latency Hardware companion Context Personal learning project Linus Torvalds Not production use

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What do people build with it?

USE CASE 1

Study how basic guitar audio effects like phasers and delays are implemented in software using straightforward C code.

USE CASE 2

Explore IIR filter implementations as a starting point for your own digital signal processing experiments.

USE CASE 3

Use the included Python audio visualizer to inspect waveforms and frequency responses of custom DSP effects.

USE CASE 4

Learn how digital effects can replicate analog guitar pedal behavior using low-latency sample-by-sample processing.

What is it built with?

CPythonIIR filtersDigital Signal Processing

How does it compare?

	torvalds/audionoise	taviso/loadlibrary	riscv-collab/riscv-gnu-toolchain
Stars	4,374	4,483	4,486
Language	C	C	C
Setup difficulty	moderate	hard	hard
Complexity	3/5	4/5	4/5
Audience	developer	researcher	developer

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · moderate Time to first run · 30min

Companion hardware pedal project exists, the software runs standalone for audio processing experiments.

License terms are not described in the explanation, check the repository directly.

So what is it?

AudioNoise is a personal learning project by Linus Torvalds, the creator of Linux, focused on building digital guitar audio effects in software. It is a companion to a separate hardware guitar pedal project he worked on, but here the goal is to experiment with digital audio processing without worrying about the physical hardware. The effects implemented are intentionally simple. Rather than modern AI-driven cabinet simulations or complex frequency analysis, this code uses IIR filters and delay loops. IIR filters are a basic signal-processing technique that can mimic the behavior of analog circuits, like the kind found in a real guitar phaser pedal. Each effect processes one audio sample at a time with no added delay, which matches the low-latency behavior of the hardware pedal it grew out of. The project is explicitly described as a learning exercise. The author notes he is new to digital audio processing and the implementations reflect that, being straightforward rather than clever. A Python-based audio visualizer tool is also included, the README notes that tool was written with AI assistance since the author is less comfortable with Python than with hardware electronics. This is not a production audio tool and the README is clear that it should not be taken seriously as such. It is a hobby project shared publicly as the author works through the basics of digital signal processing.

Copy-paste prompts

Prompt 1

Explain how the IIR filter in audionoise implements a guitar phaser effect. What are the coefficients doing and how do they mimic an analog circuit?

Prompt 2

How does audionoise process audio one sample at a time with no added latency? Show me the main processing loop in C.

Prompt 3

I want to add a simple reverb effect to audionoise following the existing delay loop pattern. What C code structure would I use?

Prompt 4

How do I use the Python visualizer in audionoise to plot the frequency response of one of the IIR filter effects?

Prompt 5

What is the difference between how audionoise handles audio versus a modern DSP library? Why does it process sample by sample?

Frequently asked questions