3 research outputs found
Few-shot Class-incremental Audio Classification Using Stochastic Classifier
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. The proposed model consists of an embedding
extractor and a stochastic classifier. The former is trained in base session
and frozen in incremental sessions, while the latter is incrementally expanded
in all sessions. Two datasets (NS-100 and LS-100) are built by choosing samples
from audio corpora of NSynth and LibriSpeech, respectively. Results show that
our method exceeds four baseline ones in average accuracy and performance
dropping rate. Code is at https://github.com/vinceasvp/meta-sc.Comment: 5 pages, 3 figures, 4 tables. Accepted for publication in INTERSPEECH
202