Self-Supervised Graphs for Audio Representation Learning With Limited Labeled Data
Amir Shirian, Krishna Somandepalli, Tanaya Guha
Abstract
Large-scale databases with high-quality manual labels are scarce in audio domain. We thus explore a self-supervised graph approach to learning audio representations from highly limited labelled data. Considering each audio sample as a graph node, we propose a subgraph-based framework with novel self-supervision tasks to learn effective audio representations. During training, subgraphs are constructed by sampling the entire pool of available training data to exploit the relationship between the labelled and unlabeled audio samples. During inference, we use random edges to alleviate the overhead of graph construction. We evaluate our model on three benchmark audio datasets spanning two tasks: acoustic event classification and speech emotion recognition. We show that our semi-supervised model performs better or on par with fully supervised models and outperforms several competitive existing models. Our model is compact and can produce generalized audio representations robust to different types of signal noise.