Reducing MP3 compression bias in music datasets via codec-aware reconstruction

I built a tool to improve decoding of MP3 files (LAME encoded) reducing systematic codec induced bias in audio datasets.

Rather than denoising, it treats reconstruction as a disambiguation problem: MP3 encoding is non-injective, so the observed signal corresponds to a distribution of plausible originals. The model approximates this as a Bayesian inference problem induced by the compression process itself, selecting a coherent signal consistent with both codec structure and musical priors.

What it can help with?

• clearer hi-hats / cymbals
• sharper transients (less “smear”)
• reducing typical MP3 artifacts (swishy / pre-echo stuff)

What it’s not?

• not magic “restore the original track”
• not really meant for random YouTube rips or heavily re-encoded audio
• works best on consistent medium-bitrate MP3s (like 96-224 kbps CBR)

I put up:

• a web demo (kinda slow 😅)
• fully open-source repo (you can (and should) run it locally)

👉 Demo: https://audiode.theivanr.duckdns.org/
👉 Repo: https://github.com/theIvanR/ADE-MP3

** Performance vs stock decoder on unseen data **