Multimodal Self-Supervised Learning of General Audio Representations

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.

References in corpus (9)

• Learning Transferable Visual Models From Natural Language Supervision
• VoxCeleb: a large-scale speaker identification dataset
• Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
• Data-Efficient Image Recognition with Contrastive Predictive Coding
• Automatic acoustic detection of birds through deep learning: the first Bird Audio Detection challenge
• Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
• Towards Learning a Universal Non-Semantic Representation of Speech
• LEAF: A Learnable Frontend for Audio Classification
• Multi-Format Contrastive Learning of Audio Representations

Cited by in corpus (5)

• COCOA: Cross Modality Contrastive Learning for Sensor Data
• BYOL for Audio: Exploring Pre-trained General-purpose Audio Representations
• Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting Recognition
• Towards Learning Universal Audio Representations
• An empirical study of weakly supervised audio tagging embeddings for general audio representations