Sound Event Detection in Domestic Environments with Weakly Labeled Data and Soundscape Synthesis

@inproceedings{Turpault2019SoundED,
  title={Sound Event Detection in Domestic Environments with Weakly Labeled Data and Soundscape Synthesis},
  author={Nicolas Turpault and Romain Serizel and Ankit Shah and Justin Salamon},
  booktitle={DCASE},
  year={2019}
}
This paper presents Task 4 of the Detection and Classification of Acoustic Scenes and Events (DCASE) 2019 challenge and provides a first analysis of the challenge results. The task is a followup to Task 4 of DCASE 2018, and involves training systems for large-scale detection of sound events using a combination of weakly labeled data, i.e. training labels without time boundaries, and strongly-labeled synthesized data. The paper introduces Domestic Environment Sound Event Detection (DESED… 
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Methods Citations
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A benchmark of submissions to Detection and Classification Acoustic Scene and Events 2021 Challenge (DCASE) Task 4 representing a sampling of the state-of-the-art in Sound Event Detection task is proposed and results show that systems adapted to provide coarse segmentation outputs are more robust to different target to non-target signal-to-noise ratio and to time localization of the original event.
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This work proposes a CRNN system exploiting unlabeled data with semi-supervised learning based on the “Mean teacher” method, in combination with data augmentation to overcome the limited size of the training dataset and to further improve the performances.
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Cross-domain sound event detection: from synthesized audio to real audio Technical Report
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This technical report describes some of the system information submitted to dcase2020 task4 Sound Event Detection in Domestic Environments, and proposes a DACRNN network for joint learning of sound event detection and domain adaptation.
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Sound Event Detection in Synthetic Domestic Environments
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A comparative analysis of the performance of state-of-the-art sound event detection systems based on the results of task 4 of the DCASE 2019 challenge, where submitted systems were evaluated on a series of synthetic soundscapes that allow us to carefully control for different soundscape characteristics.
Sound Event Detection and Separation: A Benchmark on Desed Synthetic Soundscapes
  • Nicolas Turpault, R. Serizel, J. Salamon
  • Physics, Computer Science
    ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2021
TLDR
It is shown that temporal localization of sound events remains a challenge for SED systems and that reverberation and non-target sound events severely degrade system performance.
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This paper uses a forward-backward convolutional recurrent neural network for tagging and pseudo labeling followed by tag-conditioned sound event detection (SED) models which are trained using strong pseudo labels provided by the FBCRNN and introduces a strong label loss in the objective of the F BCRNN to take advantage of the strongly labeled synthetic data during training.
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