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Sound Event Localization and Detection Using Imbalanced Real and Synthetic Data via Multi-Generator.

Yeongseo Shin1, Chanjun Chun1

  • 1Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a novel sound event localization and detection (SELD) method. The approach effectively handles imbalanced real and synthetic data using a multi-generator and achieves improved performance over baseline models.

Keywords:
residual convolutional neural networksound event detectionsound localizationtransformer

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Area of Science:

  • Acoustics and Signal Processing
  • Machine Learning
  • Artificial Intelligence

Background:

  • Sound event localization and detection (SELD) is crucial for understanding acoustic environments.
  • The Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Task 3 faces challenges with imbalanced real and synthetic data.
  • Training models on imbalanced datasets can lead to biased performance, favoring the majority data class.

Purpose of the Study:

  • To propose a novel SELD method capable of effectively utilizing imbalanced real and synthetic datasets.
  • To address the challenge of data imbalance in SELD tasks, particularly in the context of DCASE 2022 Task 3.
  • To enhance the performance of SELD systems by developing a robust training strategy and neural network architecture.

Main Methods:

  • A multi-generator approach was developed to sample real and synthetic data at a specific rate within a single batch, mitigating data imbalance.
  • The proposed method integrates a residual convolutional neural network (RCNN) with a transformer encoder for processing real spatial sound scenes.
  • Data augmentation techniques, including SpecAugment and time-frequency masking, were applied to enhance the dataset.
  • Ensemble models were created by selecting and combining the best-performing individual models based on various structures and hyperparameters.

Main Results:

  • The proposed SELD method, utilizing the multi-generator strategy, demonstrated improved performance compared to the baseline model.
  • Both the single model and the ensemble model achieved superior results, indicating the effectiveness of the proposed approach.
  • The integration of RCNN and transformer encoder architectures contributed to enhanced sound event localization and detection capabilities.

Conclusions:

  • The developed multi-generator strategy is effective in addressing data imbalance in SELD tasks.
  • The proposed RCNN and transformer encoder-based neural network architecture yields state-of-the-art performance for SELD.
  • The findings suggest a promising direction for improving SELD systems in real-world acoustic scenarios with limited real data.