I don’t really follow your logic, how else would you propose to shape the audio that is not “just an effect”.

Your analogy to real life does not take into account that the audio source itself is moving, so their is an extra variable outside of just stereo signal -which is what spatial audio is modelling

And your muffling example sounds a bit over simplified maybe? My understanding is that the spatial stuff is produced by phase shifting the LR signals slightly

Finally why not go further? “I don’t listen to speaker audio because it’s all just effects and mirages to sound like a real sound, what only 2^16 discrete positions the diaphragm can be in” :p

Before becoming president

I don’t think the story implies clout was used for this. Just a play on words to make the conductor think there would be a crowd boarding rather than an individual

Do you read a song on demand when you are singing the lyrics verbatim?

deleted by creator

It’s definitely not indexed, we use RAG architectures to add indexing to data stores that we want the model to have direct access to, the relevant information is injected directly in the context (prompt). This can somewhat be equated to short term memory

The rest of the information is approximated in the weights of the neural network which gives the model general knowledge and intuition…akin to long term memory

You seem a bit troll-y too but I’ll bite.

The only one suggesting to add women because they are women is the edgy beb. The change I want to see is edgies being made to feel small. I have achieved based on their anemic come back :)

The person starting this thread wants to see the world you’re suggesting I create. And in doing so has opened a whole new class of YouTuber for our dear OP to explore…the analogy about fish, fishing and teachers come to mind

Ooh edgy, say more

This is an extremely odd outlook to have. Good luck with it.

Unfortunately the answer to your question is to not post at all, though if your contributions are worthwhile then that is not an excellent solution

This sounds like a fun project “samples of chemistry”

There’s is a huge difference though.

That being one is making hardware and the other is copying books into your training pipeline though

The copy occurs in the dataset preparation.

Privacy preserving federated learning is a thing - essentially you train a local model and send the weight updates back to Google rather than the data itself…but also it’s early days so who knows what vulnerabilities may exist

You could try dexed, it’s a YamahaDX7 clone https://github.com/asb2m10/dexed/releases

You need rebase instead. Merge just creates useless commits and makes the diffs harder to comprehend (all changes are shown at once, but with rebase you fix the conflicts in the commit where they happened)

Then instead of your branch of branch strat you just rebase daily into main and you’re golden when it comes time to PR

For me the infinity subscription bypass stopped working so I finally made the switch