静寂から構造を学ぶ：環境音によるシーン構造の学習
Structure from Silence: Learning Scene Structure from Ambient Sound
静かな屋内環境で収録した音声とRGB-Dのペアデータセットを構築し、音声のみから壁までの距離を推定するモデルを訓練。さらに音と画像を対応付ける自己教師あり学習でマルチモーダル表現を獲得できることを示した。
著者: Ziyang Chen, Xixi Hu, Andrew Owens
分類: cs.SD, cs.CV, cs.MM, cs.RO, eess.AS
原文アブストラクト
From whirling ceiling fans to ticking clocks, the sounds that we hear subtly vary as we move through a scene. We ask whether these ambient sounds convey information about 3D scene structure and, if so, whether they provide a useful learning signal for multimodal models. To study this, we collect a dataset of paired audio and RGB-D recordings from a variety of quiet indoor scenes. We then train models that estimate the distance to nearby walls, given only audio as input. We also use these recordings to learn multimodal representations through self-supervision, by training a network to associate images with their corresponding sounds. These results suggest that ambient sound conveys a surprising amount of information about scene structure, and that it is a useful signal for learning multimodal features.
