A mixture-of-experts model combines binaural filters online via implicit localization to enable real-time enhancement or suppression of moving sound sources while preserving natural binaural cues.
Mixture-of-Experts Framework for Field-of-View Enhanced Signal-Dependent Binauralization of Moving Talkers
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abstract
We propose a novel mixture of experts framework for field-of-view enhancement in binaural signal matching. Our approach enables dynamic spatial audio rendering that adapts to continuous talker motion, allowing users to emphasize or suppress sounds from selected directions while preserving natural binaural cues. Unlike traditional methods that rely on explicit direction-of-arrival estimation or operate in the Ambisonics domain, our signal-dependent framework combines multiple binaural filters in an online manner using implicit localization. This allows for real-time tracking and enhancement of moving sound sources, supporting applications such as speech focus, noise reduction, and world-locked audio in augmented and virtual reality. The method is agnostic to array geometry offering a flexible solution for spatial audio capture and personalized playback in next-generation consumer audio devices.
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cs.SD 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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Mixture-of-Experts Framework for Field-of-View Enhanced Signal-Dependent Binauralization of Moving Talkers
A mixture-of-experts model combines binaural filters online via implicit localization to enable real-time enhancement or suppression of moving sound sources while preserving natural binaural cues.