51–60 of 101 posts
Are we almost converting music to MIDI at this point?
As I understand it the model is learning the landscape of sound combinations that are interesting to humans and as such there will be no combination of raw bytes in the recorded file that will result in white noise (for example) being heard because this is never trained for.
What if it was though?
This doesn't have much of a use case. - Can't use it in telephony (obvious application for low bitrates); phone handsets and headsets don't have the power to do it in real time. - Very small files of good quality would be useful in tiny embedded systems that have low flash space: but what systems of that type have the processing power for decoding? Very low storage more or less goes hand in hand with weak processing.…
Check out the citations of Encodec (Facebook's open sourced audio codec) for more examples: https://scholar.google.com/scholar?cites=1126914113099467682...
This doesn't have much of a use case. - Can't use it in telephony (obvious application for low bitrates); phone handsets and headsets don't have the power to do it in real time. - Very small files of good quality would be useful in tiny embedded systems that have low flash space: but what systems of that type have the processing power for decoding? Very low storage more or less goes hand in hand with weak processing.…
Am I missing something in thinking that this could be alleviated like every other compression algorithm by implementing it via a hardware codec?
Makes me happy to see DAC getting built on! Thanks!
Always love a good bellard ship. This is definitely better than some of the others out there. I threw together some comparisons here at 7kb/s for mp3/opus/aac: https://non.io/TSAC-Comparisons Happy to add other comparisons if others want any. Overall, it's FAR better at these lower bit rates, but that doesn't mean it's necessarily good. One issue I see off the bat is that volume is fairly inconsistent in the output f…
This doesn't have much of a use case. - Can't use it in telephony (obvious application for low bitrates); phone handsets and headsets don't have the power to do it in real time. - Very small files of good quality would be useful in tiny embedded systems that have low flash space: but what systems of that type have the processing power for decoding? Very low storage more or less goes hand in hand with weak processing.…
Not now but in another 5 years they will start to and in 10 years all new ones will probably have the power for this. I find it really exciting although it will consume more battery to run that; but if that is less than the radio antenna requires then it might make sense.
Earlier quoted context omitted.
Am I missing something in thinking that this could be alleviated like every other compression algorithm by implementing it via a hardware codec?
It's a neural network, not a traditional compression algorithm. It would be difficult to implement this efficiently in an ASIC AFAIK, but if there are any hardware designers that disagree please chime in.
How well does it work on any song outside of its training set?
This doesn't have much of a use case. - Can't use it in telephony (obvious application for low bitrates); phone handsets and headsets don't have the power to do it in real time. - Very small files of good quality would be useful in tiny embedded systems that have low flash space: but what systems of that type have the processing power for decoding? Very low storage more or less goes hand in hand with weak processing.…
I often wonder, how much of the data currently in circulation will be lost at some point? HDD/SSD last a couple of years. Most of the data in the cloud will be copied over, but some will be lost. If you extrapolate to a 1000, 1 000 000 years, how much will remain? Will something survive the civilization collapse? I guess most people don't care, but some will ...
One way to make data mediums last longer is to make them lower density, and for that such super-low bitrate could be useful.