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Anomaly Detection in Raw Audio Using Deep Autoregressive Networks
Author(s)
Date Issued
2019-05-17
Date Available
2020-05-06T08:33:44Z
Abstract
Anomaly detection involves the recognition of patterns outside of what is considered normal, given a certain set of input data. This presents a unique set of challenges for machine learning, particularly if we assume a semi-supervised scenario in which anomalous patterns are unavailable at training time meaning algorithms must rely on non-anomalous data alone. Anomaly detection in time series adds an additional level of complexity given the contextual nature of anomalies. For time series modelling, autoregressive deep learning architectures such as WaveNet have proven to be powerful generative models, specifically in the field of speech synthesis. In this paper, we propose to extend the use of this type of architecture to anomaly detection in raw audio. In experiments using multiple audio datasets we compare the performance of this approach to a baseline autoencoder model and show superior performance in almost all cases.
Sponsorship
Science Foundation Ireland
Other Sponsorship
Insight Research Centre
Type of Material
Conference Publication
Publisher
IEEE
Copyright (Published Version)
2019 IEEE
Language
English
Status of Item
Peer reviewed
Part of
2019 IEEE International Conference on Acoustics, Speech and Signal Processing: Proceedings
Conference Details
The 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, 12-17 May 2019
ISBN
978-1-4799-8131-1
This item is made available under a Creative Commons License
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