As a computer tech and audio enthusiast, I have been interested in the so-called "Artificial Intelligences" (actually machine learning and transformers, nowdays) and I have collected some interesting free/open resources for the HyMPS project in the AUDIO section \ AI-based page.

Among these, in the Enhancers subsection, I had collected some self-called "restorers" that have - through statistical inference - the goal to regenerate close-to-lossless from compressed audio files (e.g. MP3).

For some time I was relatively uninterested in them (in the meantime I helped an Intel engineer choose which upscaler to bring into their OpenVINO™ AI Plugins for Audacity) but recently I discovered that Apollo models has obtained quite interesting qualitative results:



This model - named MP3 restore - has been trained to "delossify" < 128Kbps sources and can be tested here:

- https://huggingface.co/spaces/patriotyk/Apollo
- https://colab.research.google.com/g...lob/main/Apollo_Audio_Restoration_Colab.ipynb

...it would be very interesting to organize a so-called "blind test" (in addition to a null one of course) to fully understand its effectiveness in relation to various musical genres.