  Large scale databases with high-quality manual annotations 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 that can 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
databases, and two tasks: acoustic event detection and speech emotion
recognition. Our semi-supervised model performs better or on par with fully
supervised models and outperforms several competitive existing models. Our
model is compact (240k parameters), and can produce generalized audio
representations that are robust to different types of signal noise.
