Unsupervised and Semi-Supervised Few-Shot Acoustic Event Classification

Few-shot Acoustic Event Classification (AEC) aims to learn a model to recognize novel acoustic events using very limited labeled data. Previous works utilize supervised pre-training as well as meta-learning approaches, which heavily rely on labeled data. Here, we study unsupervised and semi-supervised learning approaches for few-shot AEC. Our work builds upon recent advances in unsupervised repres...