Just tried a few 128kbps. Seems to work pretty well.

 

No disagreement here, simply alerting to another option as I know many people here use MVSEP with registered accounts so have access to the other file types. Also for those who are confused about what this is I thought the links might be helpful especially with the amount of user generated demos they have on the site.

For the Apollo Enhancers in the "Model Type" there is an option labelled Universal Super Resolution (by MVSEP Team) and for the AudioSR (Super Resolution) there's the ability to adjust the Cutoff (hz)... not sure if that answers that comment.

 

I tried some of this delossifying myself. I guess it won't all of a sudden create a crisp Hi-Fi sound out of your 1901 wax cylinder - frequencies gone means frequencies gone. But maybe the delossifyng process generates data for users to add frequencies to, while keeping it lossless?

 

Well, exactly as in video upscaling, neural networks does NOT "restore" anything but GUESSES missing frequencies of lossy encoding.

In other words they GENERATES/INVENT (with higher "fidelity" larger are trained) what has been trashed.

I honestly think it's still too early (as Detlef Kroll - the author of AudioDelossifier - claimed here, more extensive training would most likely lead to mutch better results), but the path is signed.

Anyway I do also believe that training should be performed on obsolete lossy audio encodings to be even more effective.

I've found where the (Pytorch) models are stored thanks to the Apollo-Colab-Inference repo:
- the original one (aka "MP3 Enhancer") is here;
- the "Universal model for any lossy files by Lew" in his own repository.

I'm pushing Ryan Metcalfe to integrate them into Intel's OpenVINO™ AI Plugins for Audacity: fingers crossed !

Well you could do this if you had enough similar recordings and their high-fidelity versions to train the neural network in order to “understand” the correlation between the two signals.

I'm interested in these approaches 'cause I'd like to train a neural network to generate a near-losless stereo signal as if it were recorded "directly from the PA mixer" by inferencing on multiple cameras/smartphones lossy audio tracks.

I've found this interesting study/project, that has a similar - but somewhat different - goal:
Audio Enhancement from Multiple Crowdsourced Recordings

Inference GENERATES a completely new signal from the lossy audio one by GUESSING the (probable) lossless source for.

The concept behind this machine/deep learning approach is pretty simple: if a neural network "understand", through a HUGE number of examples (= files), which mathematical/statistical correlation occurs between a lossless signal and its lossy version, then it will then be able to generate the opposite.

 

Last edited: Jun 27, 2025 at 10:11 AM

@ArticStorm I'm really sorry I missed the reply to your question!

It all depends on the dataset hugeness the machine learning is feeded with: more "examples" usually brings to better results, since the (mathematical/statistical) correlation between source and destination signals correlation is more clearly "understood" by the neural network.

I strongly recommend you and @ClarSum to check this very clear and simple explaination video by Leo Gibson on how (NAM, but it's basically the same for all) neural networks works:

Last but not least, I invite anyone to check - and, why not, contribute to - the (WIP) AI-based audio resources collection I've realized for the HyMPS project.

 

Last edited: Jun 27, 2025 at 9:59 AM