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…
103 Citations
SpecAugment for Sound Event Detection in Domestic Environments using Ensemble of Convolutional Recurrent Neural Networks
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By combining the proposed methods, sound event detection performance can be enhanced, compared with the baseline algorithm, and performance evaluation shows that the proposed method provides detection results of 40.89% for event-based metrics and 66.17% for segment-based metric.
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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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JOINT ACOUSTIC AND SUPERVISED INFERENCE FOR SOUND EVENT DETECTION Technical Report
- Computer Science
- 2020
A sound event detection system for the task 4 of DCASE2020 is built by integrating several methods such signal enhancement and event boundary detection, and built five systems by integrating these methods with supervised system trained by using Mean Teacher model.
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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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Performance analysis was performed on parameters, such as the feature, moving average parameter, weight of the consistency cost function, ramp-up length, and maximum learning rate, using the data of DCASE 2020 Task 4.
Sound Event Detection in Synthetic Domestic Environments
- Computer ScienceICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- 2020
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.
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- Physics, Computer ScienceICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- 2021
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.
Self-Trained Audio Tagging and Sound Event Detection in Domestic Environments
- Computer ScienceDCASE
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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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