Learning Self-Supervised Audio-Visual Representations for Sound Recommendations

We propose a novel self-supervised approach for learning audio and visual representations from unlabeled videos, based on their correspondence. The approach uses an attention mechanism to learn the relative importance of convolutional features extracted at different resolutions from the audio and visual streams and uses the attention features to encode the audio and visual input based on their correspondence. We evaluated the representations learned by the model to classify audio-visual correlation as well as to recommend sound effects for visual scenes. Our results show that the representations generated by the attention model improves the correlation accuracy compared to the baseline, by 18% and the recommendation accuracy by 10% for VGG-Sound, which is a public video dataset. Additionally, audio-visual representations learned by training the attention model with cross-modal contrastive learning further improves the recommendation performance, based on our evaluation using VGG-Sound and a more challenging dataset consisting of gameplay video recordings.