Siamese Networks for 1D Signal Identification

In material sciences, X-ray diffraction (XRD) or nuclear magnetic response (NMR) are methods to generate one-dimensional signals, describing intensities over an angle or a chemical  shift. Each material has a characteristic profile and unknown samples are typically matched to known references. Automatic classification of one-dimensional signal patterns is a non- trivial task due to background noise and varying positions of measured intensities in identical probes. Convolutional Neural Networks prove to be particularly suitable, a limitation,  though, is that adding new classes requires retraining. However, continuous discovery of new materials requires possibilities for easy classextension. Siamese Neural Networks are able  to extend data set classes easily and are popular in the field of face recognition, where new faces are constantly added to the database of references. In this paper, we apply  Siamese networks to one-dimensional XRD-data for the first time and discuss the opportunities and challenges as well as areas of application. We show that Siamese networks are well  suited for the transfer between XRD datasets, achieving an accuracy of 99% for materials not present in the training dataset.