191–200 of 207 posts
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I don't understand how Bluetooth has made it this far for phone calls. It has two modes: unintelligible quality and bad latency, or decent quality and ludicrous latency. The entire standard is an advertisement for Airpods.
And that's on top of a ridiculously complex stack of protocols. I don't understand it either.
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Off topic but how do you put images on ipfs and what’s the advantage over e.g. Imgur?
Cloudflare are kindly hosting [1] a free HTTP gateway for the IPFS [2] network. So I can host an image myself on a server with IPFS, and Cloudflare will cache it for me. This is better than Imgur because the latter has been redirecting users to annoying "social" pages with ads instead of showing them the actual image, at least in some cases. I also can't be sure whether Imgur recompresses your uploads or not - I assu…
What I want to know is whether Lyra takes any longer to encode than the alternatives. Because as far as I can tell, nobody cares in the slightest about latency. Phone calls are getting to be like writing postcards to each other. Speak in a whole paragraph. Wait several seconds for the latency to clear. Then the other party responds with a whole paragraph, waits several seconds for the latency to clear... Improvements…
I had the pleasure of using a real landline just before the pandemic. Honest wire-to-wire connection between two ranches, so no silly VOIP steps between. It was fantastic. You don't appreciate how much latency is destroying our ability to communicate verbally until you go back to the old way. One example is arguing. It's no wonder people used to be able to argue with one another on a telephone. You could raise your v…
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I don't understand how Bluetooth has made it this far for phone calls. It has two modes: unintelligible quality and bad latency, or decent quality and ludicrous latency. The entire standard is an advertisement for Airpods.
What platform are you on? iOS/android/windows/macos/linux?
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Google Chrome has a latency of 20ms to just repeat back audio on the local device . That is, with no networking, and no processing, it takes 20ms for any information to from microphone back out to speakers.
Even the default Windows audio device "listen to yourself" option has some serious latency.
What I want to know is whether Lyra takes any longer to encode than the alternatives. Because as far as I can tell, nobody cares in the slightest about latency. Phone calls are getting to be like writing postcards to each other. Speak in a whole paragraph. Wait several seconds for the latency to clear. Then the other party responds with a whole paragraph, waits several seconds for the latency to clear... Improvements…
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But that is the thing, what if it isn't a codec? What if it is simply a set of model parameters, a generative model, and a stream of fiducial bits which trigger the model? We have already seen some of this with generative models that let you generate voices that sound like speaker data used to train the model right? What if, instead of say "i-frames" (or what ever their equivalent would be in an audio codec) you sent…
I think he's saying that if it is in fact a generative model, we will see significant differences when we try different languages.
What I'm getting at is this, do they use the sample as a training data set with a streamlined model generation algorithm so that they can send new initial model parameters as a blob before the rest of the data arrives?
It has my head spinning but the possibilities seem pretty tantalizing here.
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I think he's saying that if it is in fact a generative model, we will see significant differences when we try different languages.
I think I understand what he is saying, what I am struggling with is why would a 'sound' GAN care about different languages when an 'image' GAN doesn't care about different images? What I'm getting at is this, do they use the sample as a training data set with a streamlined model generation algorithm so that they can send new initial model parameters as a blob before the rest of the data arrives? It has my head spinn…
If they use any generative model in their codec, they had to train it first, offline, on some dataset. They can't possibly train it equally well on all languages, so we should be able to tell the difference in quality when comparing English to more exotic languages.
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I think I understand what he is saying, what I am struggling with is why would a 'sound' GAN care about different languages when an 'image' GAN doesn't care about different images? What I'm getting at is this, do they use the sample as a training data set with a streamlined model generation algorithm so that they can send new initial model parameters as a blob before the rest of the data arrives? It has my head spinn…
I think you would agree that a GAN, or any generative model can only generate something in the same domain as what it was trained on. If you trained on mostly on human faces with a little bit of rabbits, it's not going to generate rabbits well. If you trained it on mostly on English text and a little bit on Mandarin, it's not going to generate good text in Mandarin. Same with sounds. Different languages use different…
> If they use any generative model in their codec, they had to train it first, offline, on some dataset.
One thing I'm wondering if they have a model that can be "retrained" on the fly.
Let's assume for this discussion that you've got a model with 1024 weights in it. You train it on spoken text, all languages, just throw anything at it that is speech. That gets a you a generalized model that isn't specialized for any particular kind of speech and the results will be predictably mixed when you generate random speech from it. But if you take it, and ran a "mini" training system on just the sample of interest, so you have this general model, you digitize the speech, you run it through your trainer, now the generalized model is better at generating exactly this kind of speech agreed? So now you take the weights and generate a set of changes from the previous "generic" set, you bundle those changes in the header of the data you are sending and label them appropriately. Now you send only the data bits from the training set that were needed to activate those parts of the model that are updated. Your data product becomes (, ).
What I'm wondering is this, if every digitization is used to train the model, and you can send the model deltas in a way that the receiver can incorporate those changes in a predictable way to its local model. Can you then send just the essential features of the digitized sound and get it to re-generate by the model on the other end (which has incorporated the model deltas you sent).
Here is an analogy for how I'm thinking about this, and it can be completely wrong, just speculating. If you wanted to "transport" a human with the least number of bits you could simply take their DNA and their mental state and transmit THAT to a cloning facility. No need to digitize every scar, every bit of tissue, instead a model is used to regenerate the person and their 'state' is sent as state of mind.
That is clearly science fiction, but some of the GAN models I've played with have this "feel" where they will produce reliably consistent results from the same seed. Not exact results necessarily, but very consistent.
From that, and this article, I'm wondering if they figured out how to compute the 'seed' + 'initial conditions', given the model that will reproduce what was just digitized. If they have, then its a pretty amazing result.