Thank you for sharing! On a tangent: I'm wondering if there are any good open source models/libraries to reconstruct audio quality. I'm thinking about an end-to-end open source alternative to something like Adobe Podcast [1] to make noisy recordings sound professional. Anecdotally it's supposed to be very good. In a recent search, I haven't found anything convincing. In my naive view this tasks seems much simpler than audio generation and the demand far bigger, since not everyone has a professional audio setup ready at all times.[1] https://podcast.adobe.com/
earthnail|2 years ago
We'll also publish a webapp where you can use the denoiser for free. Mail me if you want beta access to it (email in profile).
It won't be open-source though, although the paper will of course be public. It will also only reduce noise, and not reconstruct other aspects of audio quality. However, it can do so on any audio (in particular music), not just speech like Adobe Podcast, and it fully preserves the audio quality. It's designed exactly for the use case you want: to make noisy recordings sound professional.
haywirez|2 years ago
white_beach|2 years ago
whywhywhywhy|2 years ago
Only weird thing it’s designed to be used real time but I’ve had some luck on cleaning up voice recordings replayed back through it via audio routing.
joshspankit|2 years ago
On one side the tech for literal denoising has stagnated a bit. It’s a very hard problem to remove all noise while keeping things like transients.
On the other side, AI is being rapidly developed for it’s ability to denoise by recreating the recording, just without the noise.
earthnail|2 years ago
This combination was non-trivial as training old school DSP denoisers is not easily possible. We’ll describe the math needed in our paper. We hope our publication will help the wider community work not just on denoising but also tasks like automatic mixing.
cosmok|2 years ago
spdif899|2 years ago
This video from MKBHD's studio channel dives into this topic