Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/73977
Título: | Using deep autoencoders for in-vehicle audio anomaly detection |
Autor(es): | Pereira, Pedro José Coelho, Gabriel José Dias Ribeiro, Alexandrine Matos, Luís Miguel Rocha Nunes, Eduardo Carvalho Ferreira, André Pilastri, André Luiz Cortez, Paulo |
Palavras-chave: | Anomaly Detection Audio Input Representation Deep Learning In-vehicle Data Unsupervised Learning Autoencoder |
Data: | Set-2021 |
Editora: | Elsevier |
Revista: | Procedia Computer Science |
Resumo(s): | Current developments on self-driving cars has led to an increasing interest on autonomous shared taxicabs. While most self-driving car technologies focus on the outside environment, there is also a need to provide in-vehicle intelligence (e.g., detect health and safety issues related with the current car occupants). Set within an R&D project focused on in-vehicle cockpit intelligence, the research presented in this paper addresses an unsupervised Acoustic Anomaly Detection (AAD) task. Since data is nonexistent in this domain, we first design an in-vehicle sound event data simulator that can realistically mix background audios (recorded from car driving trips) with normal (e.g., people talking, radio on) and abnormal (e.g., people arguing, cough) event sounds, allowing the generation of three synthetic in-vehicle sound datasets. Then, we explore two main sound feature extraction methods (based on a combination of three audio features and mel frequency energy coefficients) and propose a novel Long Short-Term Memory Autoencoder (LSTM-AE) deep learning architecture for in-vehicle sound anomaly detection. Competitive results were achieved by the proposed LSTM-AE when compared with two state-of-the-art methods, namely a dense Autoencoder (AE) and a two-stage clustering. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/73977 |
DOI: | 10.1016/j.procs.2021.08.031 |
ISSN: | 1877-0509 |
Versão da editora: | https://www.elsevier.com/books-and-journals/procedia |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
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Este trabalho está licenciado sob uma Licença Creative Commons