Parameter-Free Ordered Partial Match Alignment with Hidden State Time Warping

Abstract

This paper investigates an ordered partial matching alignment problem, in which the goal is to align two sequences in the presence of potentially non-matching regions. We propose a novel parameter-free dynamic programming alignment method called hidden state time warping that allows an alignment path to switch between two different planes: a “visible” plane corresponding to matching sections and a “hidden” plane corresponding to non-matching sections. By defining two distinct planes, we can allow different types of time warping in each plane (e.g., imposing a maximum warping factor in matching regions while allowing completely unconstrained movements in non-matching regions). The resulting algorithm can determine the optimal continuous alignment path via dynamic programming, and the visible plane induces a (possibly) discontinuous alignment path containing matching regions. We show that this approach outperforms existing parameter-free methods on two different partial matching alignment problems involving speech and music.

Improved LDTW Algorithm Based on the Alternating Matrix and the Evolutionary Chain Tree

“…A fundamental task regarding the time series is to measure the similarity between two given ones, which is critical to downstream works in terms of classification [ 1 , 2 , 3 , 4 , 5 ], clustering [ 6 , 7 , 8 , 9 , 10 ] and pattern recognition [ 11 , 12 , 13 , 14 ]. The dynamic time warping (DTW) [ 15 ] algorithm and its variants [ 16 , 17 , 18 ] are competent in similarity evaluation [ 19 ].…”