When do you deem your efforts fruitful? Ready for production?.Where do you focus your efforts? Audio or video? Maybe just network? Should you go for server side implementation or client side one? What about model optimizations?.Can you generate or get access to a suitable data set to use? Do you even have access to enough data?.Do these engineers know anything about media processing? How do you get them up to speed with this technology? Or is it the other way around? Getting media engineers trained in machine learning.How do you find machine learning engineers, or whatever they are called in their titles this day of the week?.The data you look at is analog in its nature, and there’s often little to no labeled data sets to work with.Ī few of the things you need to figure out here? These are two separate and far apart disciplines that need to be handled. When looking at machine learning in media processing, there’s one word that comes to mind: challenging Partially because of the technology advances, but a lot because of the pandemic. Over time, machine learning will catch up and be better. There’s so much we can do with rule engines and heuristics. My argument was this: At some point, applying more heuristics to media processing algorithms loses its appeal Almost everyone was using rule engines and heuristics at the time for all of their media processing algorithms and only a few made attempts to use machine learning. We’ve interviewed vendors to understand what they’re doing and looked at the available research. In it, we’ve reviewed the various areas where machine learning is relevant when it comes to real time communications. Two years ago, I published along with Chad Hart a report called AI in RTC. The challenge will be to get ML in WebRTC. Communication vendors are waking up to the need to invest in ML/AI in media processing.
0 Comments
Leave a Reply. |