| Paper ID | AUD-31.4 |
| Paper Title |
SOUND EVENT DETECTION BASED ON CURRICULUM LEARNING CONSIDERING LEARNING DIFFICULTY OF EVENTS |
| Authors |
Noriyuki Tonami, Ritsumeikan University, Japan; Keisuke Imoto, Doshisha University, Japan; Yuki Okamoto, Takahiro Fukumori, Yoichi Yamashita, Ritsumeikan University, Japan |
| Session | AUD-31: Detection and Classification of Acoustic Scenes and Events 6: Events |
| Location | Gather.Town |
| Session Time: | Friday, 11 June, 13:00 - 13:45 |
| Presentation Time: | Friday, 11 June, 13:00 - 13:45 |
| 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 |
In conventional sound event detection (SED) models, two types of events, namely, those that are present and those that do not occur in an acoustic scene, are regarded as the same type of the events. The conventional SED methods cannot effectively exploit the difference between the two types of events. The all time frames of sound events that do not occur in an acoustic scene are easily regarded as inactive in the scene, that is, the events are easy-to-train. The time frames of the events that are present in a scene must be classified as active in addition to inactive in the acoustic scene, that is, the events are difficult-to-train. To take advantage of the training difficulty, we apply curriculum learning into SED, where models are trained from easy- to difficult-to-train events. To utilize the curriculum learning, we propose a new objective function for SED, wherein the events are trained from easy to difficult-to-train events. Experimental results show that the F-score of the proposed method is improved by 10.09 percentage points compared with that of the conventional binary cross entropy-based SED. |
