Learning to Localize Sound Sources in Visual Scenes: Analysis and Applications

By Arda Senocak, Tae-Hyun Oh, Junsik Kim, Ming-Hsuan Yang, In-So Kweon. 2019

66 citations recorded by the source corpus.

Visual events are usually accompanied by sounds in our daily lives. However, can the machines learn to correlate the visual scene and sound, as well as localize the sound source only by observing them like humans? To investigate its empirical learnability, in this work we first present a novel unsupervised algorithm to address the problem of localizing sound sources in visual scenes. In order to achieve this goal, a two-stream network structure which handles each modality with attention mechanism is developed for sound source localization. The network naturally reveals the localized response in the scene without human annotation. In addition, a new sound source dataset is developed for performance evaluation. Nevertheless, our empirical evaluation shows that the unsupervised method generates false conclusions in some cases. Thereby, we show that this false conclusion cannot be fixed without human prior knowledge due to the well-known correlation and causality mismatch misconception. To fix this issue, we extend our network to the supervised and semi-supervised network settings via a simple modification due to the general architecture of our two-stream network. We show that the false conclusions can be effectively corrected even with a small amount of supervision, i.e., semi-supervised setup. Furthermore, we present the versatility of the learned audio and visual embeddings on the cross-modal content alignment and we extend this proposed algorithm to a new application, sound saliency based automatic camera view panning in 360 degree videos.

Subfields: Computer Vision and Pattern Recognition, Multimodal Learning, Signal Processing

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Citations in the corpus

Listen to Look: Action Recognition by Previewing Audio (2019, background): “Recent work uses audio for an array of video understanding tasks outside of action recognition, including self-supervised representation learning [43, 5, 7, 41, 33, 54], audio-visual source separation [41, 2, 13, 19, 76, 21], localizing sounds in video frames [6, 48, 55], and generating sounds from ”
HMS: Hierarchical Modality Selection for Efficient Video Recognition (2021, methodology): “These multimodal inputs are used in other video tasks such as self-supervised learning in videos [1, 25], sound localization [32, 36] and generation [54, 55] from videos as well.”