Active Audio-Visual Separation of Dynamic Sound Sources

• URL: http://arxiv.org/abs/2202.00850v1
• Date: Wed, 2 Feb 2022 02:03:28 GMT
• Title: Active Audio-Visual Separation of Dynamic Sound Sources
• Authors: Sagnik Majumder, Ziad Al-Halah, Kristen Grauman
• Abstract summary: We propose a reinforcement learning agent equipped with a novel transformer memory that learns motion policies to control its camera and microphone. We show that our model is able to learn efficient behavior to carry out continuous separation of a time-varying audio target.
• Score: 93.97385339354318
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We explore active audio-visual separation for dynamic sound sources, where an embodied agent moves intelligently in a 3D environment to continuously isolate the time-varying audio stream being emitted by an object of interest. The agent hears a mixed stream of multiple time-varying audio sources (e.g., multiple people conversing and a band playing music at a noisy party). Given a limited time budget, it needs to extract the target sound using egocentric audio-visual observations. We propose a reinforcement learning agent equipped with a novel transformer memory that learns motion policies to control its camera and microphone to recover the dynamic target audio, improving its own estimates for past timesteps via self-attention. Using highly realistic acoustic SoundSpaces simulations in real-world scanned Matterport3D environments, we show that our model is able to learn efficient behavior to carry out continuous separation of a time-varying audio target. Project: https://vision.cs.utexas.edu/projects/active-av-dynamic-separation/.

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