Multimodal Self-Supervised Learning of General Audio Representations

TL;DR

This work introduces a multimodal self-supervised framework for learning general audio representations by leveraging video as an auxiliary supervisory signal. By using low-resolution video, large batch sizes, and Mixup-style example mixing across modalities, the approach achieves state-of-the-art AudioSet performance and strong generalization across diverse audio tasks. The results show that video signals can effectively guide audio representation learning without labels, bringing unsupervised methods closer to supervised performance. The findings highlight the practicality of using unlabeled video data to train robust audio embeddings applicable to a wide range of applications.

Abstract

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.

Figures (2)

• Figure 1: Illustration of our contrastive learning framework that takes videos, waveforms, and spectrograms as inputs. Details can be found in Section \ref{['sec:method']}.
• Figure 2: Density functions of the $\beta$ distributions we consider for the mixing ratio $\alpha$.