SoundBoost Bets Big on Natural Language Music Mastering

Photo Credit: SoundBoost

Most AI mastering and mixing tools still translate creative intent into presets and sliders. SoundBoost takes a different approach: users simply describe the sound they want in plain language and refine it through conversation, just like working with a real engineer.

This article was created in collaboration with DMN partner SoundBoost.

At the center of the platform is its AI mastering engine, which replaces traditional controls with natural language input. Instead of adjusting equalization, compression, or limiting settings manually, users describe their desired outcome in plain terms. Prompts like “more analog warmth without crushing the dynamics” or “decrease drums by 2dB, set wider stereo field but keep the lows in mono” are interpreted by the system and translated into a finished master.

This approach places SoundBoost in a distinct category within the broader AI mastering space. Rather than relying on presets, the platform allows iterative dialogue. Users can refine results through ongoing prompts, effectively conversing with the mastering engine until the track aligns with their intent.

SoundBoost also supports reference-based mastering and multiple AI “engineer personas,” which emulate different stylistic approaches. Its latest AI engine further expands this workflow by allowing users to make both mixing and mastering decisions using natural language.

Prompts such as “remove vocals,” “reduce the drums by 2 dB” or “make the song louder without distorting” are interpreted directly, enabling AI mixing and AI mastering to be completed together within a single conversational workflow. Available on the web, iOS, and Android, SoundBoost further expands accessibility, especially for independent artists working outside traditional studio environments.

Alongside mastering, SoundBoost also includes a stand-alone stem separation tool designed for both production and performance contexts. The platform’s free vocal remover extends beyond basic vocal isolation, offering separation of drums, bass, guitar, piano, and additional drum elements. Users can manipulate pitch and tempo independently, detect chords, practice instruments, create adaptive metronome tracks, and export high-quality stems for remixes.

The company notes that the vocal remover is available at no cost, lowering the barrier for entry among emerging creators.

SoundBoost reports a user base now exceeding 140,000 musicians globally. The company attributes this growth to a design philosophy centered on user control. Unlike fully automated systems, SoundBoost frames AI as an assistive layer rather than a decision maker.

“Most AI mastering is a slot machine. You upload, you pull the lever, and you either like what comes out or you upload again,” shares Berkan Cesur, CEO of SoundBoost. “That’s not a tool, that’s a coin flip with a subscription. We want to give artists the ability to shape a signature sound that’s truly their own and help them reach sonic ideas they can hear in their heads but don’t yet know how to achieve technically.”

SoundBoost positions transparency and creator trust as central to its AI strategy. According to the company, all training data is legally sourced, user uploads are never used for model training, and files are deleted from its servers when users remove their projects.