Few-shot Class-incremental Audio Classification Using Stochastic Classifier

Understand

It is generally assumed that number of classes is fixed in current audio classification methods, and the model can recognize pregiven classes only.

• When new classes emerge, the model needs to be retrained with adequate samples of all classes.
• If new classes continually emerge, these methods will not work well and even infeasible.
• In this study, we propose a method for fewshot class-incremental audio classification, which continually recognizes new classes and remember old ones.