Super Learner Ensemble for Sound Classification using Spectral Features

Autor(es)

Gantert, Luana ; Sammarco, Matteo ; Detyniecki, Marcin ; Campista, Miguel Elias M. ; Moraes, IM ; Campista, MEM ; Ghamri-Doudane, Y ; Costa, LHMK ; Rubinstein, MG

• Tipo de documento: Artigo Científico
• Fonte: 2022 IEEE LATIN-AMERICAN CONFERENCE ON COMMUNICATIONS (LATINCOM); v. N/A, p. 6-pg., 2022-01-01.

Resumo

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. (AU)

• Processo FAPESP: 15/24494-8 - Comunicação e processamento de big data em nuvens e névoas computacionais
• Beneficiário: Nelson Luis Saldanha da Fonseca
• Modalidade de apoio: Auxílio à Pesquisa - Temático