多任务学习框架下的声事件定位与检测损失函数设计

Translated title of the contribution: Loss function design for sound event localization and detection based on multi-task learning

Jinbo Hu, Yin Cao, Ming Wu, Feiran Yang, Jun Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The track-wise multi-task learning approach exhibits significant efficacy in detecting overlapping sound sources for sound event localization and detection. However, as the number of predicted event classes increases, the track-wise multi-task networks often produce sparse outputs, resulting in missing alarms of sound events. To address this issue, this paper introduces an aggregated loss function, reformulating the multi-task learning framework into a single-task learning problem by coupling the activity of sound events with its Cartesian direction-of-arrival vector. Furthermore, considering the characteristics of the track-wise output format, auxiliary duplicated targets are introduced to optimize the system outputs by replicating events from active tracks into inactive ones. Experimental results on a large-scale synthetic test set with 170 event classes demonstrate that the proposed method significantly improves the performance in sound event detection, effectively reduces the missing alarm rate, and achieves substantial improvement in localization and trajectory tracking. Additionally, experimental results on the real-scene dataset demonstrate the effectiveness of the proposed methods.

Translated title of the contributionLoss function design for sound event localization and detection based on multi-task learning
Original languageChinese (Simplified)
Pages (from-to)338-345
Number of pages8
JournalShengxue Xuebao/Acta Acustica
Volume50
Issue number2
DOIs
Publication statusPublished - Mar 2025
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2025 Science Press. All rights reserved.

ASJC Scopus Subject Areas

  • Acoustics and Ultrasonics

Keywords

  • Aggregated loss
  • Auxiliary duplicated target
  • Event-independent network
  • Multi-task learning
  • Sound event localization and detection

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