  It's assumed that training data is sufficient in base session of few-shot
class-incremental audio classification. However, it's difficult to collect
abundant samples for model training in base session in some practical scenarios
due to the data scarcity of some classes. This paper explores a new problem of
fully few-shot class-incremental audio classification with few training samples
in all sessions. Moreover, we propose a method using expandable dual-embedding
extractor to solve it. The proposed model consists of an embedding extractor
and an expandable classifier. The embedding extractor consists of a pretrained
Audio Spectrogram Transformer (AST) and a finetuned AST. The expandable
classifier consists of prototypes and each prototype represents a class.
Experiments are conducted on three datasets (LS-100, NSynth-100 and FSC-89).
Results show that our method exceeds seven baseline ones in average accuracy
with statistical significance. Code is at: https://github.com/YongjieSi/EDE.
