Few Shot Audio Classification

Few-shot Audio Classification refers to the task of classifying audio signals with limited samples, aiming to achieve efficient learning and generalization with only a small amount of labeled data. This task not only needs to handle time dependencies but also address the challenge of minor differences between categories. By employing methods such as supervised meta-learning or pre-training on external data, the model's recognition ability for new categories can be enhanced, making it valuable for applications like speech recognition, emotion analysis, and environmental sound detection.