The Problem

This repo compiles a collection of simple digital audio effects for learning and prototyping, but lacks any automated build or test gate. A consultant reviewing the setup finds no CI/CD configuration, no lockfile, and a Python visualizer with deep control‑flow nesting that raises cognitive load for anyone extending the project.

What This Does

AudioNoise provides a modular set of IIR‑based digital audio effects—phaser, flanger, distortion, echo, LFO, and biquad filters—all implemented as single‑sample‑in/single‑sample‑out processes with no latency. The C headers (effect.h, biquad.h, phaser.h, flanger.h, distortion.h, echo.h, fm.h, growlingbass.h, lfo.h, tube.h, am.h, process.h) declare the effect algorithms, while four C source files (convert.c, gensin.c, and two test files) provide utilities and test vectors. A single Python visualizer (visualize.py) renders audio samples but was written through vibe‑coding; static analysis reports a maximum indentation depth of 6, making control flow hard to follow. The Makefile is the only build artifact; no pyproject.toml or package.json is present, so the Python tool has no declared dependencies.

How It Is Wired

Execution begins at the Makefile, which compiles the 18 C files (14 headers, 4 sources) into object or binary output. The effect headers are the primary integration points: process.h likely orchestrates the signal chain, while individual effect headers (phaser.h, flanger.h, etc.) implement their own all‑pass or feedback networks. The import graph contains 19 analyzed code files (18 C, 1 Python) with zero import edges and no circular dependencies, meaning visualize.py is isolated from the C modules—it touches the codebase only through data inspection, not through function calls. The most connected module is visualize (Ca 0, Ce 0, instability 0), confirming it does not import or get imported by any C component. The three test files (tests/lfo.c, tests/sincos.c, and the implicit test harness) exist but are not wired into any CI pipeline.

How To Use It

Setup: The repo contains a Makefile and a visualize.py script. No lockfile, pyproject.toml, or package.json is committed, so dependency resolution must be handled manually; the Python visualizer requires a Python interpreter but has no pinned packages documented. Configuration: No environment variables or config files are documented. The LICENSE file is present; no secrets were detected in the source. Running it: To build the C sources, invoke make from the root directory; specific targets are not documented in the tracked files, so the full set of make targets must be discovered by running make help or examining the Makefile contents. To launch the visualizer, run python visualize.py; note that the script’s nesting depth of 6 may require refactoring before extensive modification.

If the repo provides no evidence for a step, the appropriate response is to state what is missing rather than invent a plausible command.

Real-World Use

A firmware engineer prototyping a DSP‑based guitar pedal on an RP2354/TAC5112 platform can use AudioNoise as a reference for implementing IIR filters, delay loops, and LFO modulation without committing to specific hardware IRQ chains. The biquad.h and process.h headers demonstrate a clean, single‑sample processing pattern that maps directly to embedded audio callbacks. The effect headers serve as educational examples of how analog RC networks translate to digital all‑pass and feedback structures—useful for learning the math before layering in more complex processing.

Code Health & Issues

  • MEDIUM/cognitive_load – visualize.py has max indentation depth 6; control flow is hard to follow. Flatten with early returns/guard clauses or extract inner blocks.
  • No CI/CD pipeline detected – no automated build/test gate.
  • No Dockerfile or lockfile present; licence is committed, but no lockfile for Python or C dependencies.
  • 3 test files exist but are not integrated into any build or CI step.

The Bottom Line

AudioNoise is a competent learning sandbox for digital audio basics—IIR filters, delay loops, and modular effect headers are well‑structured and easy to inspect. The absence of CI, a lockfile, and the nesting complexity in visualize.py mean it isn’t production‑ready without tooling additions. It’s a solid starting point for engineers wanting to experiment with simple DSP algorithms or embed audio effects in firmware, provided they add a build gate and refactor the visualizer’s control flow.