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2021 | OriginalPaper | Buchkapitel

Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection

verfasst von : Gabriel Coelho, Pedro Pereira, Luis Matos, Alexandrine Ribeiro, Eduardo C. Nunes, André Ferreira, Paulo Cortez, André Pilastri

Erschienen in: Artificial Intelligence Applications and Innovations

Verlag: Springer International Publishing

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Abstract

Recently, there have been advances in using unsupervised learning methods for Acoustic Anomaly Detection (AAD). In this paper, we propose an improved version of two deep AutoEncoders (AE) for unsupervised AAD for six types of working machines, namely Dense and Convolutional AEs. A large set of computational experiments was held, showing that the two proposed deep autoencoders, when combined with a mel-spectrogram sound preprocessing, are quite competitive and outperform a recently proposed AE baseline. Overall, a high-quality class discrimination level was achieved, ranging from 72% to 92%.

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Vorheriges Kapitel Collaborative Edge-Cloud Computing for Personalized Fall Detection

Nächstes Kapitel Neural Network Compression Through Shunt Connections and Knowledge Distillation for Semantic Segmentation Problems

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Titel: Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection
verfasst von: Gabriel Coelho
Pedro Pereira
Luis Matos
Alexandrine Ribeiro
Eduardo C. Nunes
André Ferreira
Paulo Cortez
André Pilastri
Verlag: Springer International Publishing
Buch: Artificial Intelligence Applications and Innovations
Print ISBN: 978-3-030-79149-0

Electronic ISBN: 978-3-030-79150-6

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-79150-6_27

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