Pattern Localization in Time Series Through Signal-To-Model Alignment in Latent Space

Steven Van Vaerenbergh, Ignacio Santamaria, Victor Elvira, Matteo Salvatori

Research output: Contribution to conferencePaperpeer-review

Abstract / Description of output

In this paper, we study the problem of locating a predefined sequence of patterns in a time series. In particular, the studied scenario assumes a theoretical model is available that contains the expected locations of the patterns. This problem is found in several contexts, and it is commonly solved by first synthesizing a time series from the model, and then aligning it to the true time series through dynamic time warping. We propose a technique that increases the similarity of both time series before aligning them, by mapping them into a latent correlation space. The mapping is learned from the data through a machine-learning setup. Experiments on data from nondestructive testing demonstrate that the proposed approach shows significant improvements over the state of the art.
Original languageEnglish
Pages2711-2715
DOIs
Publication statusPublished - Apr 2018
EventICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Calgary, AB
Duration: 15 Apr 201820 Apr 2018

Conference

ConferenceICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Period15/04/1820/04/18

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