Transductive Feature Space Regularization for Few-shot Bioacoustic Event Detection

Yizhou Tan, Haojun Ai*, Shengchen Li, Feng Zhang

*Corresponding author for this work

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


In few-shot bioacoustic event detection, besides interested target events, background noises and various uninterested sound events lead to complex decision boundaries, which require regularized feature distributions in feature space. Due to the low label availability of uncertain noise events, existing few-shot learning methods with entropy-based regularizers suffer from overfitting during optimization. In this paper, we propose a transductive inference model with a prior knowledge based regularizer (PKR) to overcome the overfitting problem. We use a task-adaptive feature extractor to reconstruct a regularized feature space. A PKR is proposed to minimize the divergence between the original and reconstructed feature space. The development set of DCASE 2022 Task 5 is adopted as the experimental dataset. With the increasing iterations, the proposed model performs with long-lasting results around 55.43 F-measure, and well solves the overfitting problem in transductive inference.
Original languageEnglish
Title of host publicationProceeding of INTERSPEECH 2023
Number of pages5
Publication statusPublished - 20 Aug 2023
Event24th International Speech Communication Association, Interspeech 2023 - Dublin, Ireland
Duration: 20 Aug 202324 Aug 2023

Publication series

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
ISSN (Print)2308-457X


Conference24th International Speech Communication Association, Interspeech 2023


  • Bioacoustic Event Detection
  • Few-shot Learning
  • Transductive Inference


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