  Most existing methods for audio classification assume that the vocabulary of
audio classes to be classified is fixed. When novel (unseen) audio classes
appear, audio classification systems need to be retrained with abundant labeled
samples of all audio classes for recognizing base (initial) and novel audio
classes. If novel audio classes continue to appear, the existing methods for
audio classification will be inefficient and even infeasible. In this work, we
propose a method for few-shot class-incremental audio classification, which can
continually recognize novel audio classes without forgetting old ones. The
framework of our method mainly consists of two parts: an embedding extractor
and a classifier, and their constructions are decoupled. The embedding
extractor is the backbone of a ResNet based network, which is frozen after
construction by a training strategy using only samples of base audio classes.
However, the classifier consisting of prototypes is expanded by a prototype
adaptation network with few samples of novel audio classes in incremental
sessions. Labeled support samples and unlabeled query samples are used to train
the prototype adaptation network and update the classifier, since they are
informative for audio classification. Three audio datasets, named NSynth-100,
FSC-89 and LS-100 are built by choosing samples from audio corpora of NSynth,
FSD-MIX-CLIP and LibriSpeech, respectively. Results show that our method
exceeds baseline methods in average accuracy and performance dropping rate. In
addition, it is competitive compared to baseline methods in computational
complexity and memory requirement. The code for our method is given at
https://github.com/vinceasvp/FCAC.
