Papers › Heavily Augmented Sound Event Detection utilizing Weak Predictions
Heavily Augmented Sound Event Detection utilizing Weak Predictions
Hyeonuk Nam, Byeong-Yun Ko, Gyeong-Tae Lee, Seong-Hu Kim, Won-Ho Jung, Sang-Min Choi, Yong-Hwa Park
The performances of Sound Event Detection (SED) systems are greatly limited by the difficulty in generating large strongly labeled dataset. In this work, we used two main approaches to overcome the lack of strongly labeled data. First, we applied heavy data augmentation on input features. Data augmentation methods used include not only conventional methods used in speech/audio domains but also our proposed method named FilterAugment. Second, we propose two methods to utilize weak predictions to enhance weakly supervised SED performance. As a result, we obtained the best PSDS1 of 0.4336 and best PSDS2 of 0.8161 on the DESED real validation dataset. This work is submitted to DCASE 2021 Task4 and is ranked on the 3rd place. Code availa-ble: https://github.com/frednam93/FilterAugSED.
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Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sound Event Detection | DESED | FiltAug SED | PSDS1 | 0.4336 | #6 of 13 | Archive leaderboard | report |
| Sound Event Detection | DESED | FiltAug SED | PSDS2 | 0.8161 | #6 of 13 | Archive leaderboard | report |
| Sound Event Detection | DESED | FiltAug SED | event-based F1 score | 49.6 | #6 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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