JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

Prompting class distribution optimization dynamically for semi-supervised sound event detection

Author(s): Lijian GAO, Qing ZHU, Yaxin SHEN, Qirong MAO, Yongzhao ZHAN
Affiliation(s): School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212016, China; more
Corresponding email(s): mao_qr@ujs.edu.cn
Key Words: Prompt tuning; Class distribution learning; Semi-supervised learning; Sound event detection

Share this article to： More \|Next Paper >>>

Lijian GAO, Qing ZHU, Yaxin SHEN, Qirong MAO, Yongzhao ZHAN. Prompting class distribution optimization dynamically for semi-supervised sound event detection[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400061

@article{title="Prompting class distribution optimization dynamically for semi-supervised sound event detection",
author="Lijian GAO, Qing ZHU, Yaxin SHEN, Qirong MAO, Yongzhao ZHAN",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2400061"
}

%0 Journal Article
%T Prompting class distribution optimization dynamically for semi-supervised sound event detection
%A Lijian GAO
%A Qing ZHU
%A Yaxin SHEN
%A Qirong MAO
%A Yongzhao ZHAN
%J Frontiers of Information Technology & Electronic Engineering
%P
%@ 2095-9184
%D in press
%I Zhejiang University Press & Springer
doi="https://doi.org/10.1631/FITEE.2400061"

TY - JOUR
T1 - Prompting class distribution optimization dynamically for semi-supervised sound event detection
A1 - Lijian GAO
A1 - Qing ZHU
A1 - Yaxin SHEN
A1 - Qirong MAO
A1 - Yongzhao ZHAN
J0 - Frontiers of Information Technology & Electronic Engineering
SP -
EP -
%@ 2095-9184
Y1 - in press
PB - Zhejiang University Press & Springer
ER -
doi="https://doi.org/10.1631/FITEE.2400061"

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Semi-supervised sound event detection (SSED) tasks typically leverage a large amount of unlabeled and synthetic data to facilitate model generalization during training, reducing overfitting on a limited set of labeled data. However, the generalization training process often encounters challenges associated with noise interference introduced by pseudo-labels or domain knowledge gaps. To alleviate noise interference in class distribution learning, we propose an efficient semi-supervised class distribution learning method through dynamic prompt tuning, named prompting class distribution optimization (PADO). Specifically, when modeling real labeled data, PADO dynamically incorporates independent learnable prompt tokens to explore prior knowledge about the true distribution. Then, the prior knowledge serves as prompt information, dynamically interacting with the posterior noisy class distribution information. In this case, PADO achieves class distribution optimization while maintaining model generalization, leading to a significant improvement in the efficiency of class distribution learning. Compared with state-of-the-art (SOTA) methods on the DCASE 2019, 2020, and 2021 challenge SSED datasets, PADO demonstrates significant performance improvements. Furthermore, it is ready to be extended to other benchmark models.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

Open peer comments: Debate/Discuss/Question/Opinion

<1>

- Go to

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference