@conference {507,
	title = {Pattern Localization in Time Series through Signal-To-Model Alignment in Latent Space},
	booktitle = {2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
	year = {2018},
	month = {April},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Calgary, Alberta, Canada},
	abstract = {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 non-destructive testing demonstrate that the proposed approach shows significant improvements over the state of the art.},
	author = {Van Vaerenbergh, Steven and Santamar{\'\i}a, Ignacio and Elvira, Victor and Salvatori, Matteo}
}
