  Our goal is to collect a large-scale audio-visual dataset with low label
noise from videos in the wild using computer vision techniques. The resulting
dataset can be used for training and evaluating audio recognition models. We
make three contributions. First, we propose a scalable pipeline based on
computer vision techniques to create an audio dataset from open-source media.
Our pipeline involves obtaining videos from YouTube; using image classification
algorithms to localize audio-visual correspondence; and filtering out ambient
noise using audio verification. Second, we use this pipeline to curate the
VGGSound dataset consisting of more than 210k videos for 310 audio classes.
Third, we investigate various Convolutional Neural Network~(CNN) architectures
and aggregation approaches to establish audio recognition baselines for our new
dataset. Compared to existing audio datasets, VGGSound ensures audio-visual
correspondence and is collected under unconstrained conditions. Code and the
dataset are available at http://www.robots.ox.ac.uk/~vgg/data/vggsound/
