An Ensemble Deep-Learning System for Few-Shot Bioacoustic Event Detection

Jianqian Zhang, Miao Liu, Jing Wang*, Chenguang Hu, Jiawei Peng, Kaige Li

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Human are good at identifying new objects from only a few number of samples. Inspired by the rapid learning ability of humans, few-shot bioacoustic event detection task based on machine learning method has attracted more attention lately. In this paper, we formulated an ensemble deep-learning system to address the task, incorporating an enhanced prototypical network and transductive inference methodology. We have submitted our models to Task 5 of the Detection and Classification of Acoustic Scenes and Events 2022 (DCASE2022) challenge, and got F-measure of 64.8% on the validation set as our best score. In the final competition, our system got F-measure of 44.3% on the evaluation set.

Original languageEnglish
Title of host publicationProceedings of 2023 7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages192-197
Number of pages6
ISBN (Electronic)9798350359145
DOIs
Publication statusPublished - 2023
Event7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023 - Quzhou, China
Duration: 10 Nov 202312 Nov 2023

Publication series

NameProceedings of 2023 7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023

Conference

Conference7th Asian Conference on Artificial Intelligence Technology, ACAIT 2023
Country/TerritoryChina
CityQuzhou
Period10/11/2312/11/23

Keywords

  • bioacoustic event detection
  • DCASE
  • few-shot task
  • prototypical networks
  • transductive inference

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