| Paper ID | AUD-13.6 |
| Paper Title |
SOUND EVENT DETECTION BY CONSISTENCY TRAINING AND PSEUDO-LABELING WITH FEATURE-PYRAMID CONVOLUTIONAL RECURRENT NEURAL NETWORKS |
| Authors |
Chih-Yuan Koh, You-Siang Chen, Yi-Wen Liu, Mingsian Bai, National Tsing Hua University, Taiwan |
| Session | AUD-13: Detection and Classification of Acoustic Scenes and Events 2: Weak supervision |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-CLAS] Detection and Classification of Acoustic Scenes and Events |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Due to the high cost of large-scale strong labeling, sound event detection (SED) using only weakly-labeled and unlabeled data has drawn increasing attention in recent years. To exploit large amount of unlabeled in-domain data efficiently, we applied three semi-supervised learning strategies: interpolation consistency training (ICT), shift consistency training (SCT), and weakly pseudo-labeling. In addition, we propose FP-CRNN, a convolutional recurrent neural network (CRNN) which contains feature-pyramid (FP) components, to leverage temporal information by utilizing features at different scales. Experiments were conducted on DCASE 2020 task 4. In terms of event-based F-measure, these approaches outperform the official baseline system, at 34.8%, with the highest Fmeasure of 48.0% achieved by an FP-CRNN that was trained with the combination of all three strategies. |
