Diffraction-Aware Sound Localization for a Non-Line-of-Sight Source

An, Inkyu; Lee, Doheon; Choi, Jung‐Woo; Manocha, Dinesh; Yoon, Sung‐Eui

doi:10.48550/arxiv.1809.07524

Cited by 3 publications

(1 citation statement)

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“…However, estimating the accurate acoustic material is an expensive task. Recent SSL papers considering reflection [5] and diffraction [6] only use the 3-D geometry of indoor rooms for the real-time processing, without taking into account acoustic material information.…”

Section: Introductionmentioning

confidence: 99%

Real-time 3-D Mapping with Estimating Acoustic Materials

Kim

Kwon

Yoon

2020

2020 IEEE/SICE International Symposium on System Integration (SII)

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This paper proposes a real-time system integrating an acoustic material estimation from visual appearance and an on-the-fly mapping in the 3-dimension. The proposed method estimates the acoustic materials of surroundings in indoor scenes and incorporates them to a 3-D occupancy map, as a robot moves around the environment. To estimate the acoustic material from the visual cue, we apply the state-of-the-art semantic segmentation CNN network based on the assumption that the visual appearance and the acoustic materials have a strong association. Furthermore, we introduce an update policy to handle the material estimations during the online mapping process. As a result, our environment map with acoustic material can be used for sound-related robotics applications, such as sound source localization taking into account various acoustic propagation (e.g., reflection).

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Section: Introductionmentioning

confidence: 99%