Temporal Convolution-based Hybrid Model Approach with Representation Learning for Real-Time Acoustic Anomaly Detection

Author:

Dissanayaka Sahan1ORCID,Wickramasinghe Manjusri1ORCID,Marasinghe Pasindu1ORCID

Affiliation:

1. Department of Computation and Intelligent Systems, University of Colombo School of Computing, Sri Lanka

Publisher

ACM

Reference23 articles.

1. Charu C. Aggarwal. 2016. Outlier Analysis (2nd ed.). Springer Publishing Company, Incorporated.

2. Shaojie Bai, J. Zico Kolter, and Vladlen Koltun. 2018. An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling. CoRR abs/1803.01271 (2018). arXiv:1803.01271http://arxiv.org/abs/1803.01271

3. Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2014. Representation Learning: A Review and New Perspectives. arxiv:1206.5538 [cs.LG]

4. A Review on Outlier/Anomaly Detection in Time Series Data

5. Raghavendra Chalapathy and Sanjay Chawla. 2019. Deep Learning for Anomaly Detection: A Survey. CoRR abs/1901.03407 (2019). arXiv:1901.03407http://arxiv.org/abs/1901.03407

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