  We present a multimodal framework to learn general audio representations from
videos. Existing contrastive audio representation learning methods mainly focus
on using the audio modality alone during training. In this work, we show that
additional information contained in video can be utilized to greatly improve
the learned features. First, we demonstrate that our contrastive framework does
not require high resolution images to learn good audio features. This allows us
to scale up the training batch size, while keeping the computational load
incurred by the additional video modality to a reasonable level. Second, we use
augmentations that mix together different samples. We show that this is
effective to make the proxy task harder, which leads to substantial performance
improvements when increasing the batch size. As a result, our audio model
achieves a state-of-the-art of 42.4 mAP on the AudioSet classification
downstream task, closing the gap between supervised and self-supervised methods
trained on the same dataset. Moreover, we show that our method is advantageous
on a broad range of non-semantic audio tasks, including speaker identification,
keyword spotting, language identification, and music instrument classification.
