Papers › Dual Knowledge Distillation for Efficient Sound Event Detection
Dual Knowledge Distillation for Efficient Sound Event Detection
Yang Xiao, Rohan Kumar Das
Sound event detection (SED) is essential for recognizing specific sounds and their temporal locations within acoustic signals. This becomes challenging particularly for on-device applications, where computational resources are limited. To address this issue, we introduce a novel framework referred to as dual knowledge distillation for developing efficient SED systems in this work. Our proposed dual knowledge distillation commences with temporal-averaging knowledge distillation (TAKD), utilizing a mean student model derived from the temporal averaging of the student model's parameters. This allows the student model to indirectly learn from a pre-trained teacher model, ensuring a stable knowledge distillation. Subsequently, we introduce embedding-enhanced feature distillation (EEFD), which involves incorporating an embedding distillation layer within the student model to bolster contextual learning. On DCASE 2023 Task 4A public evaluation dataset, our proposed SED system with dual knowledge distillation having merely one-third of the baseline model's parameters, demonstrates superior performance in terms of PSDS1 and PSDS2. This highlights the importance of proposed dual knowledge distillation for compact SED systems, which can be ideal for edge devices.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Sound Event Detection | DESED | SE-CRNN-16 with DualKD | PSDS1 | 0.474 | #2 of 13 | Archive leaderboard | report |
| Sound Event Detection | DESED | SE-CRNN-16 with DualKD | PSDS2 | 0.698 | #2 of 13 | Archive leaderboard | report |
| Sound Event Detection | DESED | SE-CRNN-16 with DualKD | event-based F1 score | 55.6 | #2 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.
Methods
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