matchering-mastering

Match a target audio mix's spectral and loudness characteristics to a reference track.

1|Updated Nov 24, 2025

Search Tags:#workflow #python #reference #audio #cpp #mastering #matchering

Ready to run

npx skills add https://github.com/SpiralCloudOmega/DevTeam6 --skill matchering-mastering

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: matchering-mastering
Source: https://github.com/SpiralCloudOmega/DevTeam6/tree/main/.github/skills/ai-integration/matchering-mastering
Command: npx skills add https://github.com/SpiralCloudOmega/DevTeam6 --skill matchering-mastering

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates reference-based audio mastering by matching spectral balance and loudness of a target track to a chosen reference, enabling consistent sound across projects.

Core Features & Use Cases

Spectral matching: Align tonal balance to the reference track.
Python/C++ bridge: Integrates a Python mastering engine with a DAW workflow via a lightweight bridge.
Batch processing: Master multiple tracks or albums to ensure tonal continuity.

Quick Start

Export the target mix at 48 kHz WAV, select a reference, and run the mastering bridge to produce a matched master.

Frequently Asked Questions about matchering-mastering

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate matching my audio mix to a reference track's spectral balance?▼

Reference-based audio mastering matches a target track's spectral and loudness characteristics to a chosen reference. It aligns tonal balance and loudness using a Python mastering engine and subprocess bridge to ensure consistent sound across projects.

Can I master an entire album for consistent tonal balance in a single batch?▼

Yes, batch processing supports mastering multiple tracks or entire albums for consistent tonal balance and loudness continuity across all songs within a project workflow.

What sample rate and format do I need to export my target mix at for audio mastering?▼

You need to export your target mix as a 48 kHz WAV file. Selecting a reference track and running the mastering bridge produces a matched master output with true-peak limiting at this specific sample rate.

Does this Python audio mastering workflow require Matchering 2.0?▼

Yes, the workflow requires Matchering 2.0, a Python-based mastering engine. A subprocess bridge orchestrates the target, reference, and output audio at 48 kHz with true-peak limiting to produce the matched master.

What limitations exist when applying true-peak limiting to a matched audio master?▼

The workflow applies true-peak limiting at a 48 kHz sample rate during output. It relies entirely on the reference track's spectral and loudness characteristics, meaning a poor reference selection will yield a poor matched master.