Emastered vs MusicLM

Emastered masters your tracks online in seconds using an AI engine built with Grammy-winning engineers. Upload an MP3, AIFF, or WAV file and the system analyzes your song, applies EQ, compression, and loudness processing, then returns a polished master ready for streaming. You can preview the result against your original and download from the homepage for free before subscribing.

Unlike generic loudness plugins, Emastered picks processing per track and learns from each master. Paid plans add Mastering Options so you can tweak compression, EQ, stereo width, and volume and save presets. The Studio bundle also bundles stem separation, distribution, ATOM synth, PitchPro vocal tuning, and Linkify in one subscription instead of paying separate tools.

Independent musicians, producers, and bedroom artists use Emastered when they need radio-ready masters without hiring a mastering engineer. The platform reports 3 million plus artists and 30 million plus tracks mastered. Billboard, Vice, and MusicRadar have featured the service.

MusicLM is a Google Research audio generation model that creates music from text captions at 24 kHz. The public examples page hosts sample clips for prompts like arcade soundtracks, reggaeton-EDM fusions, and relaxing jazz, plus longer story-mode generations that shift styles across timed segments. Google released the MusicCaps dataset of 5,500 music-text pairs alongside the paper.

Unlike consumer apps that ship one prompt box, MusicLM was published as a research demo with pre-generated samples rather than a login product. Its headline trick is joint text-and-melody conditioning: you can hum or whistle a tune and have the model re-render it in a new genre described in text. Story mode chains multiple captions so the music evolves across sections.

MusicLM matters as the research foundation behind Google's later Lyria music models, but this page is for listening to published examples, not creating new tracks interactively. Researchers and musicians study it for long-form consistency, painting-to-music conditioning, and melody transfer results documented in the 2023 paper.