UNDERSTANDING THE DIFFERENCE AND HOW TO USE THEM
Machine learning tools are transforming music production, but terms like "AI Mixing" and "AI Mastering" are often confused. While AI mastering applies final polish to a single stereo file, AI mixing attempts to balance individual multitracks. Here is what you need to know about current AI capabilities.
Understand the Tech • Optimize Your Workflow
To understand AI's role, you first must understand the traditional workflow.
Mixing involves taking all the individual elements of a song (kick drum, bass, lead vocals, backing vocals, synths and guitars) and blending them together. The mixer adjusts volume levels, pans instruments left/right and applies EQ, compression, reverb and delay to create a cohesive stereo file.
Mastering takes that single, finalized stereo mixdown and prepares it for commercial release. It involves broad EQ strokes, maximizing loudness to streaming standards (-14 to -8.5 LUFS) and ensuring the track translates well across all speaker systems.
AI mixing is a developing field, but it faces significant challenges compared to AI mastering.
A mix can have anywhere from 10 to 150 individual tracks. An AI mixer must make subjective decisions: Should the vocal be loud and upfront like a pop song or buried in reverb like shoegaze? This artistic choice makes fully automated AI mixing complex.
Currently, AI mixing works best as an assistant. Smart EQs can analyze a vocal and cut harsh frequencies. Unmasking plugins listen to a bass and kick drum to carve out space so they don't clash.
Unlike mixing, mastering has clear mathematical standards (LUFS, True Peak), making it ideal for machine learning.
If your mix is finished, let Mixify’s AI engine deliver a release-ready master in seconds.
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