Temporal alignment of human motion performing similar activities has been a topic of recent interest due to its many applications in animation, tele-rehabilitation or activity recognition. This paper presents generalized time warping (GTW), an extension of dynamic time warping (DTW) for temporally aligning multi-modal sequences from multiple subjects performing similar activities. GTW solves th...

Dynamic time warping (DTW) is a fundamental technique in time series analysis for comparing one curve to another using a flexible time-warping function. However, it was designed to compare a single pair of curves. In many applications, such as in metabolomics and image series analysis, alignment is simultaneously needed for multiple pairs. Because the underlying warping functions are often rela...

The purpose of this paper is to present a Dynamic Time Warping technique which reduces significantly the data processing time and memory size of multi-dimensional time series sampled by the biometric smart pen device BiSP. The acquisition device is a novel ballpoint pen equipped with a diversity of sensors for monitoring the kinematics and dynamics of handwriting movement. The DTW algorithm has...

This extended abstract descries KETI’s submission to the query-by-singing/humming (QbSH) task of MIREX 2012. Our QbSH system is based on dynamic time warping (DTW) and frame-based pitch sequence. Our system reduces false alarm to combine the distances of multiple DTW processes. To improve the performance, asymmetric DTW with boundary condition, compensation, and distances insensitive to the err...

The ubiquity of sequences in many domains enhances significant recent interest in sequence learning, for which a basic problem is how to measure the distance between sequences. Dynamic time warping (DTW) aligns two sequences by nonlinear local warping and returns a distance value. DTW shows superior ability in many applications, e.g. video, image, etc. However, in DTW, two points are paired ess...

Dynamic time warping (DTW) has been widely used in various pattern recognition and time series data mining applications. However, as examples will illustrate, both the classic DTW and its later alternative, derivative DTW, may fail to align a pair of sequences on their common trends or patterns. Furthermore, the learning capability of any supervised learning algorithm based on classic/derivativ...

Dynamic Time Warping (DTW) and Hidden Markov Model (HMM) are two well-studied non-linear sequence alignment ( or, pattern matching) algorithm. The research trend transited from DTW to HMM in approximately 1988-1990, since DTW is deterministic and lack of the power to model stochastic signals. In this report, I make a comprehensive literature study into this transition, and show that DTW and sto...

A tight lower-bounding measure for dynamic time warping (DTW) distances for univariate time series was introduced in [Keogh 2002] and a proof for its lower-bounding property was presented. Here we extend these findings to allow lower-bounding of DTW distances for multivariate time series.

A time synchronization system is a helpful tool for different applications, such as language education and speech therapy. We present a system that performs temporal alignment of two utterances of the same phrase. The system consists of two parts. In the first part the time warping function is determined with Dynamic Time Warping (DTW). In the second part the time scale of one utterance is modi...