Problem
Audio mastering workflows become hard to trust when analysis, chain decisions, rendering, QC, and reports live in disconnected tools.
Tool Case Study
AI Audio Mastering
Audio analysis, mastering-chain planning, DSP reports, and release packaging for AI-assisted mastering workflows.
Public PreviewAudio analysis and mastering workflowPublic Release
Solution
Built a Community layer for upload, loudness, spectrum, dynamics, stereo analysis, and manual workflow foundations, while keeping the Pro decision engine separate.
Result
Published the open-source analysis layer with screenshots, release assets, and clear Community / Pro boundaries without presenting it as a finished hosted service.
Current capabilities
Loudness, spectrum, dynamics, and stereo analysis
Mastering-chain planning
QC-style report outputs
Preparing
A clearer product boundary for the Pro direction
More release-proof examples and walkthroughs
Cleaner onboarding for non-audio-engineering users
How it works
01
Upload or reference an audio file, then run loudness, spectrum, dynamics, and stereo analysis.
02
Generate mastering-chain planning and QC-style report outputs without bundling private Pro logic.
03
Keep heavy media renders and release files outside the website, linked as external proof.
Build proof
Community repository and release assets are available publicly.
Community / Pro boundaries are documented so the site does not overclaim private functionality.
The website presents a lightweight media preview instead of shipping heavy audio artifacts.
Current status
Public Preview. This page presents a lightweight case study and links outward for repositories, releases, demos, or heavy artifacts instead of bundling them into the website runtime.
Availability
Community analysis layer is visible, while the fuller product direction remains in planning.
Terminology
loudness analysisspectrum analysisdynamicsstereo imagemastering chainQC reportrelease asset
Stack
PythonStreamlitDSP modulespyloudnormDemucsReports