SoundLoc3D: Invisible 3D Sound Source Localization and Classification Using a Multimodal RGB-D Acoustic Camera

• URL: http://arxiv.org/abs/2412.16861v2
• Date: Sun, 29 Dec 2024 05:06:53 GMT
• Title: SoundLoc3D: Invisible 3D Sound Source Localization and Classification Using a Multimodal RGB-D Acoustic Camera
• Authors: Yuhang He, Sangyun Shin, Anoop Cherian, Niki Trigoni, Andrew Markham,
• Abstract summary: SoundLoc3D treats the task as a set prediction problem, each element in the set corresponds to a potential sound source. We demonstrate the efficiency and superiority of SoundLoc3D on large-scale simulated dataset.
• Score: 61.642416712939095
• License:
• Abstract: Accurately localizing 3D sound sources and estimating their semantic labels -- where the sources may not be visible, but are assumed to lie on the physical surface of objects in the scene -- have many real applications, including detecting gas leak and machinery malfunction. The audio-visual weak-correlation in such setting poses new challenges in deriving innovative methods to answer if or how we can use cross-modal information to solve the task. Towards this end, we propose to use an acoustic-camera rig consisting of a pinhole RGB-D camera and a coplanar four-channel microphone array~(Mic-Array). By using this rig to record audio-visual signals from multiviews, we can use the cross-modal cues to estimate the sound sources 3D locations. Specifically, our framework SoundLoc3D treats the task as a set prediction problem, each element in the set corresponds to a potential sound source. Given the audio-visual weak-correlation, the set representation is initially learned from a single view microphone array signal, and then refined by actively incorporating physical surface cues revealed from multiview RGB-D images. We demonstrate the efficiency and superiority of SoundLoc3D on large-scale simulated dataset, and further show its robustness to RGB-D measurement inaccuracy and ambient noise interference.

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