Rushe, EllenEllenRusheMacNamee, BrianBrianMacNamee2020-05-062020-05-062019 IEEE2019-05-17978-1-4799-8131-1http://hdl.handle.net/10197/11368The 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, Brighton, United Kingdom, 12-17 May 2019Anomaly 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.en© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Anomaly detectionDeep learningRaw audioWaveNetConference Publication10.1109/ICASSP.2019.86834142019-11-04SFI/15/CDA/3520https://creativecommons.org/licenses/by-nc-nd/3.0/ie/