  Audio classification plays a crucial role in speech and sound processing
tasks with a wide range of applications. There still remains a challenge of
striking the right balance between fitting the model to the training data
(avoiding overfitting) and enabling it to generalise well to a new domain.
Leveraging the transferability of contrastive learning, we introduce Audio
Contrastive-based Fine-tuning (AudioConFit), an efficient approach
characterised by robust generalisability. Empirical experiments on a variety of
audio classification tasks demonstrate the effectiveness and robustness of our
approach, which achieves state-of-the-art results in various settings.
