Matchering

Matchering is an open-source audio matching and mastering toolkit that masters a target track by analyzing and matching it to a reference track. It adjusts key perceptual and technical properties so the processed target more closely resembles the reference.

Key Features

• Reference-based mastering workflow using a target and a reference audio file
• Matches loudness (RMS), frequency response (tonal balance), peak level, and stereo width
• Brickwall limiting via the built-in Hyrax limiter
• Usable as a Python library for integration into pipelines and products
• Containerized deployment option (Docker image) for running as a web application

Use Cases

• Quickly create a consistent sound across multiple songs in an album or EP
• Produce alternate masters that emulate different commercial references
• Integrate automated reference mastering into developer workflows or services

Limitations and Considerations

• Quality depends heavily on selecting an appropriate reference track and starting mix
• Some audio formats (for example MP3) may require FFmpeg to be installed
• Requires a reasonably capable machine (the project recommends at least 4 GB RAM)

Matchering is best suited for creators and developers who want repeatable, reference-driven mastering with an open implementation. It can be used interactively or embedded into automated processing workflows.