| Paper ID | MLSP-37.3 |
| Paper Title |
IMPROVING DEEP LEARNING SOUND EVENTS CLASSIFIERS USING GRAM MATRIX FEATURE-WISE CORRELATIONS |
| Authors |
Antonio Joia Neto, Andre G. C. Pacheco, Diogo Carbonera Luvizon, Samsung, Brazil |
| Session | MLSP-37: Pattern Recognition and Classification 2 |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Machine Learning for Signal Processing: [MLR-PRCL] Pattern recognition and classification |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In this paper, we propose a new Sound Event Classification(SEC) method which is inspired in recent works for out-of-distribution detection. In our method, we analyse all the activations of a generic CNN in order to produce feature representations using Gram Matrices. The similarity metrics are evaluated considering all possible classes, and the final prediction is defined as the class that minimizes the deviation with respect to the features seeing during training. The proposed approach can be applied to any CNN and our experimental evaluation of four different architectures on two datasets demonstrated that our method consistently improves the baseline models. |
