audio
The new Bose Lifestyle Collection is whole-home audio that won't take up your whole room
Gear Audio Speakers The new Bose Lifestyle Collection is whole-home audio that won't take up your whole room Featuring a soft-edged speaker, soundbar, and subwoofer, the new WiFi-connected series wants to sound big without looking imposing. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. We may earn revenue from the products available on this page and participate in affiliate programs. In a townhouse on New York's Upper West Side, Bose revealed its new Lifestyle speaker collection through a multi-story demo involving quite a few stairs and equally ascending audio. From a company so well-known for actively canceling noise, this was about generating buzz.
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The overlooked driver of digital transformation
Clear, reliable audio is no longer optional, say Genevieve Juillard, CEO of IDC, and Chris Schyvinck, president and CEO at Shure. When business leaders talk about digital transformation, their focus often jumps straight to cloud platforms, AI tools, or collaboration software. Yet, one of the most fundamental enablers of how organizations now work, and how employees experience that work, is often overlooked: audio. As Genevieve Juillard, CEO of IDC, notes, the shift to hybrid collaboration made every space, from corporate boardrooms to kitchen tables, meeting-ready almost overnight. In the scramble, audio quality often lagged, creating what research now shows is more than a nuisance. Poor sound can alter how speakers are perceived, making them seem less credible or even less trustworthy. Audio is the gatekeeper of meaning," stresses Julliard. "If people can't hear clearly, they can't understand you. And if they can't understand you, they can't trust you, and they can't act on what you said. And no amount of sharp video can fix that. For Shure, which has spent a century advancing sound technology, the implications extend far beyond convenience.
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Self-Supervised Visual Acoustic Matching
Acoustic matching aims to re-synthesize an audio clip to sound as if it were recorded in a target acoustic environment. Existing methods assume access to paired training data, where the audio is observed in both source and target environments, but this limits the diversity of training data or requires the use of simulated data or heuristics to create paired samples. We propose a self-supervised approach to visual acoustic matching where training samples include only the target scene image and audio---without acoustically mismatched source audio for reference. Our approach jointly learns to disentangle room acoustics and re-synthesize audio into the target environment, via a conditional GAN framework and a novel metric that quantifies the level of residual acoustic information in the de-biased audio. Training with either in-the-wild web data or simulated data, we demonstrate it outperforms the state-of-the-art on multiple challenging datasets and a wide variety of real-world audio and environments.