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Sound Event Detection by Pseudo-Labeling in Weakly Labeled Dataset.

Chungho Park1, Donghyeon Kim1, Hanseok Ko1

  • 1Department of Electronics and Electrical Engineering, Korea University Seoul, Seoul 136-713, Korea.

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|December 28, 2021
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Summary

This study introduces an improved weakly labeled sound event detection model using gated linear units and dilated convolutions. The novel approach enhances accuracy in real-world audio by better handling unknown sounds and achieving significant performance gains.

Keywords:
dilated convolutiongated linear unit (GLU)noise labelnoise losssegmentation maskweakly labeled sound event detection (WSED)

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Area of Science:

  • Audio signal processing
  • Machine learning for acoustics
  • Deep learning for sound event detection

Background:

  • Weakly labeled sound event detection (WSED) is crucial for efficient dataset creation.
  • Current deep learning models struggle with real-time audio due to limitations in feature extraction and handling unknown sounds.
  • Convolutional Neural Networks (CNNs) in WSED often lack sufficient receptive fields and fail to prioritize important features.

Purpose of the Study:

  • To develop a more robust and efficient WSED model for real-world audio streams.
  • To address limitations in feature importance highlighting and receptive field size in existing CNN-based WSED approaches.
  • To improve the model's ability to distinguish between target and unknown sound events.

Main Methods:

  • Implemented a novel model incorporating Gated Linear Units (GLU) and dilated convolutions to enhance feature representation.
  • Introduced pseudo-label-based learning with 'noise labels' and 'noise loss' to effectively classify and separate unknown sound content.
  • Utilized a combination of DCASE 2018 task 1 (acoustic scenes) and task 2 (sound events) data for experimentation.

Main Results:

  • The proposed sound event detection (SED) model achieved state-of-the-art F1 scores: 59.7% at 0 SNR, 64.5% at 10 SNR, and 65.9% at 20 SNR.
  • Demonstrated significant performance improvements over the baseline model, with increases of 17.7%, 16.9%, and 16.5% at the respective SNR levels.
  • The pseudo-labeling strategy effectively reduced the impact of unknown environmental sounds on detection accuracy.

Conclusions:

  • The proposed WSED model effectively overcomes limitations of previous methods by improving feature learning and handling of unknown audio content.
  • The integration of GLU, dilated convolutions, and pseudo-labeling offers a promising direction for robust sound event detection in challenging acoustic environments.
  • The experimental results validate the model's superior performance and its potential for practical applications in real-time audio analysis.