How New Developments in AI Improve the Audio Experience

How New Developments in AI Improve the Audio Experience

You’re on a virtual call. It’s a key meeting that you need to pay attention to, maybe even speak in. Unfortunately, as you start to chime in with your contribution, other call attendees let you know they can’t understand you. The space you’re in is large, causing your voice to echo. You manage to relocate to a smaller room, but it’s inconvenient and still echoes slightly. What can you do? Well, with newer AI-driven audio technology, it’s never been easier to be loud and clear.

It’s possible to create a crisp, quality audio experience with current hardware and software, of course. But picking up that equipment can be costly, plus setting it up to get that level of quality takes time and knowledge. To remedy this, companies like NVIDIA and Krisp have developed AI solutions that make noise filtering a breeze. As they further refine their offerings, these AI-powered products even provide things like transcription services and meeting notes.


SteelSeries, a computer peripherals manufacturer, demonstrates their Sonar AI audio technology

Audio-based AI has already shown significant potential in a variety of media applications, including addressing a common issue for video creators: Extracting a specific audio source from a piece of media. Separating specific audio out from media can be a chore, and like mentioned previously, takes technical knowledge to achieve. Recently, Adobe has given a sneak peek at a new tool in development, called Project Sound Lift, that greatly reduces the effort this process takes. Users can upload media to this tool, which then divides various audio sources into separate tracks, allowing the user to utilize or transform the audio as they desire.

AI is an impressive and burgeoning frontier of technology, albeit one still in its infancy. What we understand as AI now isn’t a machine ominously becoming sentient, a one-size-fits-all solution, or crucially, a replacement for real human input. As more companies implement their own technologies, it’s important for them to keep in mind that aiming for a specific goal often yields the best results for users. That being said, these focused applications of AI (maybe more accurately called machine learning, in most instances) can make a real difference in rote processes. What would otherwise be a laborious effort for most becomes an intuitive, approachable endeavor that opens the way to even more opportunity.

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