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Smart city article details
| Title | Super Learner Ensemble For Sound Classification Using Spectral Features |
|---|---|
| ID_Doc | 53619 |
| Authors | Gantert L.; Sammarco M.; Detyniecki M.; Campista M.E.M. |
| Year | 2022 |
| Published | 2022 IEEE Latin-American Conference on Communications, LATINCOM 2022 |
| DOI | http://dx.doi.org/10.1109/LATINCOM56090.2022.10000704 |
| Abstract | Audio samples have emerged as a trend for monitoring and improving decision-making in smart cities, medical applications, and environmental event detections. This paper proposes a Super Learner ensemble application in two scenarios: To distinguish urban from domestic sounds, and detect abnormal samples in industrial machines. The Super Learner combines supervised classifiers to detect abnormal samples or determine a class of an event from spectral features extracted from original sounds. We study the impact on time processing and performance of varying the number of K-folds in the cross-validation step using the Environmental Sound Classification (ESC-50) and Malfunctioning Industrial Machine Investigation and Inspection (MIMII) datasets. The performance evaluation demonstrates that RF is the best classifier in the ESC-50 dataset and SVM in the MIMII dataset. However, the Super Learner reaches AUC and F1-Score values near the best algorithm in the majority of cases analyzed, representing the best tradeoff solution. © 2022 IEEE. |
| Author Keywords | Industrial Internet of Things; Machine Learning; Smart cities; Super Learner ensemble |
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