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Related Experiment Video

Updated: Jun 14, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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A Study of Improved Two-Stage Dual-Conv Coordinate Attention Model for Sound Event Detection and Localization.

Guorong Chen1, Yuan Yu1, Yuan Qiao1

  • 1School of Intelligent Technology and Engineering, Chongqing University of Science and Technology, No. 20, Daxuecheng East Road, Shapingba District, Chongqing 401331, China.

Sensors (Basel, Switzerland)
|August 29, 2024
PubMed
Summary

This study introduces a new model for Sound Event Detection and Localization (SELD) that improves accuracy by using a Dual-conv Coordinate Attention Module and enhanced recurrent units. The TDCAM model significantly outperforms baseline methods on the TAU Spatial Sound Events 2019 dataset.

Keywords:
coordinate attentionsound event detectionsound event detection and localizationsound source localization

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Signal Processing

Background:

  • Sound Event Detection and Localization (SELD) is challenging due to overlapping sound events in time and space.
  • Existing two-stage models struggle with temporal processing limitations.

Purpose of the Study:

  • To develop an improved SELD model addressing the limitations of current approaches.
  • To enhance feature selection and temporal modeling capabilities for SELD.

Main Methods:

  • Introduced the SELD-oriented Two-Stage Dual-conv Coordinate Attention Model (TDCAM).
  • Integrated Dual-conv Coordinate Attention Module (DCAM) for feature enhancement.
  • Utilized a two-layer Bi-directional Gated Recurrent Unit (Bi-GRU) and data augmentation (frequency/time masks).

Main Results:

  • TDCAM significantly improved SELD performance compared to the baseline two-stage network.
  • Ablation studies confirmed the effectiveness of DCAM and the two-layer Bi-GRU structure.
  • The model demonstrated enhanced modeling and generalization for temporal features.

Conclusions:

  • The proposed TDCAM model offers a significant advancement in SELD.
  • The combination of DCAM and enhanced recurrent structures effectively addresses SELD challenges.
  • The findings provide a strong foundation for future research in multi-modal event detection and localization.