Given the ubiquity of time series data, the data mining community has spent significant time investigating the best time series similarity measure to use for various tasks and domains. After more than a decade of extensive efforts, there is increasing evidence that Dynamic Time Warping (DTW) is very difficult to beat. Given that, recent efforts have focused on making the intrinsically slow DTW ...

We investigate usage of dynamic time warping (DTW) algorithm for aligning raw signal data from MinION sequencer. DTW is mostly using for fast alignment for selective sequencing to quickly determine whether a read comes from sequence of interest. We show that standard usage of DTW has low discriminative power mainly due to problem with accurate estimation of scaling parameters. We propose a simp...

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...

Dynamic Time Warping (DTW) is an algorithm to align temporal sequences with possible local non-linear distortions, and has been widely applied to audio, video and graphics data alignments. DTW is essentially a point-to-point matching method under some boundary and temporal consistency constraints. Although DTW obtains a global optimal solution, it does not necessarily achieve locally sensible m...

With Microsoft’s launch of Kinect in 2010, and release of Kinect SDK in 2011, numerous applications and research projects exploring new ways in human-computer interaction have been enabled. Gesture recognition is a technology often used in human-computer interaction applications. Dynamic time warping (DTW) is a template matching algorithm and is one of the techniques used in gesture recognition...

Nearest neighbor (NN) classifier with dynamic time warping (DTW) is considered to be an effective method for time series classification. The performance of NN-DTW is dependent on the DTW constraints because the NN classifier is sensitive to the used distance function. For time series classification, the global path constraint of DTW is learned for optimization of the alignment of time series by...

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...

In this paper we describe the system proposed by NNI (NWPUNTU-I2R) team for the QUESST task within the Mediaeval 2014 evaluation. To solve the problem, we used both dynamic time warping (DTW) and symbolic search (SS) based approaches. The DTW system performs template matching using subsequence DTW algorithm and posterior representations. The symbolic search is performed on phone sequences gener...

Subsequence similarity search is one of the most important problems of time series data mining. Nowadays there is empirical evidence that Dynamic Time Warping (DTW) is the best distance metric for many applications. However in spite of sophisticated software speedup techniques DTW still computationally expensive. There are studies devoted to acceleration of the DTW computation by means of paral...