Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/73560| Título: | Deep dense and convolutional autoencoders for machine acoustic anomaly detection |
| Autor(es): | Coelho, Gabriel Pereira, Pedro Matos, Luis Ribeiro, Alexandrine Nunes, Eduardo C. Ferreira, André Cortez, Paulo Pilastri, André |
| Palavras-chave: | Acoustic anomaly detection Unsupervised learning Autoencoders Convolutional neural network |
| Data: | Jun-2021 |
| Editora: | Springer |
| Revista: | IFIP Advances in Information and Communication Technology |
| Citação: | Coelho, G., Pereira, P., Matos, L., Ribeiro, A., et. al (2021). Deep Dense and Convolutional Autoencoders for Machine Acoustic Anomaly Detection. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 337-348). Springer |
| Resumo(s): | 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%. |
| Tipo: | Artigo em ata de conferência |
| URI: | https://hdl.handle.net/1822/73560 |
| ISBN: | 978-3-030-79149-0 |
| e-ISBN: | 978-3-030-79150-6 |
| DOI: | 10.1007/978-3-030-79150-6_27 |
| ISSN: | 1868-4238 |
| Versão da editora: | https://link.springer.com/chapter/10.1007/978-3-030-79150-6_27 |
| Arbitragem científica: | yes |
| Acesso: | Acesso aberto |
| Aparece nas coleções: |
Ficheiros deste registo:
| Ficheiro | Descrição | Tamanho | Formato | |
|---|---|---|---|---|
| aiai2-dcase.pdf | 1,44 MB | Adobe PDF | Ver/Abrir |
