Hybrid Attention-Based Prototypical Networks for Few-Shot Sound Classification
You Wang, David V. Anderson
Abstract
In recent years, prototypical networks have been widely used in many few-shot learning scenarios. However, as a metric-based learning method, their performance often degrades in the presence of bad or noisy embedded features, and outliers in support instances. In this paper, we introduce a hybrid attention module and combine it with prototypical networks for few-shot sound classification. This hybrid attention module consists of two blocks: a feature-level attention block, and an instance-level attention block. These two attention mechanism can highlight key embedded features and emphasize crucial support instances respectively. The performance of our model was evaluated using the ESC-50 dataset and the noiseESC-50 dataset. The model was trained in a 10-way 5-shot scenario and tested in four few-shot cases, namely 5-way 1-shot, 5-way 5-shot, 10-way 1-shot, and 10-way 5-shot. The results demonstrate that by adding the hybrid attention module, our model outperforms the baseline prototypical networks in all four scenarios.