Few-Shot Acoustic Event Detection Via Meta Learning

Bowen Shi; Ming Sun; Krishna C. Puvvada; Chieh-Chi Kao; Spyros Matsoukas; Chao Wang

ICASSP 2020accepted0 citations

Few-Shot Acoustic Event Detection Via Meta Learning

Bowen Shi, Ming Sun, Krishna C. Puvvada, Chieh-Chi Kao, Spyros Matsoukas, Chao Wang

Abstract

We study few-shot acoustic event detection (AED) in this paper. Few-shot learning enables detection of new events with very limited labeled data. Compared to other research areas like computer vision, few-shot learning for audio recognition has been under-studied. We formulate few-shot AED problem and explore different ways of utilizing traditional supervised methods for this setting as well as a variety of meta-learning approaches, which are conventionally used to solve few-shot classification problem. Compared to supervised baselines, meta-learning models achieve superior performance, thus showing its effectiveness on generalization to new audio events. Our analysis including impact of initialization and domain discrepancy further validate the advantage of meta-learning approaches in few-shot AED.

BibTeX

@inproceedings{icassp2020_fewshotacoustice,
  title = {Few-Shot Acoustic Event Detection Via Meta Learning},
  author = {Bowen Shi and Ming Sun and Krishna C. Puvvada and Chieh-Chi Kao and Spyros Matsoukas and Chao Wang},
  booktitle = {ICASSP 2020},
  year = {2020}
}