Data streams are pervasive in many modern applications, and there is a pressing need to develop techniques for their efficient management. In this paper we consider real-valued streams and deal with the problem of reporting in real-time all the instants in which their distance falls below a given threshold. Current distance measures, such as Euclidean and Dynamic Time Warping (DTW ), either are...

A method commonly used to “time-normalize” gait data (here referred to as linear length normalization [LLN]) is to linearly convert the trajectory’s time axis from the experimentally-recorded time units to an axis representing percentage of the gait cycle. However, other time-normalization techniques are also possible, such as dynamic time warping [DTW] and derivative dynamic time warping [DDTW...

We present two heuristics for speeding up a time series alignment algorithm that is related to dynamic time warping (DTW). In previous work, we developed our multisegment alignment algorithm to answer similarity queries for toxicogenomic time-series data. Our multisegment algorithm returns more accurate alignments than DTW at the cost of time complexity; the multisegment algorithm is O(n(5)) wh...

This paper presents the use of Dynamic Time Warping (DTW) for measuring prosodic differences between variable-sized sentences. This methodological study may apply to various prosodic functions, accented or expressive speech. Both the structuring and attitudinal functions of prosody are investigated here. We evaluated the relevance of three prosodic (dis)similarity measures to account for percei...

The Dynamic Time Warping (DTW) is a popular similarity measure between time series. The DTW fails to satisfy the triangle inequality and its computation requires quadratic time. Hence, to find closest neighbors quickly, we use bounding techniques. We can avoid most DTW computations with an inexpensive lower bound (LB Keogh). We compare LB Keogh with a tighter lower bound (LB Improved). We find ...

Dynamic Time Warping (DTW) has been widely used in time series domain as a distance function for similarity search. Several works have utilized DTW to improve the classification accuracy as it can deal with local time shiftings in time series data by non-linear warping. However, some types of time series data do have several segments that one segment should not be compared to others even though...

This paper proposes a new investigation on Gaussian mixture model (GMM) by comparing it with some preliminary experiments on multilayered perceptron network (MLP) with backpropagation learning algorithm (BKP) and dynamic time warping (DTW) techniques on Thai text-dependent speaker identification system. Three major identification engines are conducted on 50 speakers with isolated digits 0-9. Tr...

With this presentation, we want to introduce the recently started TRIGRAPH project. The aim of this project is to develop techniques that can be used for forensic writer identification, using recent advances in pattern recognition and image processing, new insights in automatically derived handwriting features, user interface development, and innovations in forensic writer identification system...

In this paper, we present a new hybrid approach for isolated spoken word recognition using Hidden Markov Model models (HMM) combined with Dynamic time warping (DTW). HMM have been shown to be robust in spoken recognition systems. We propose to extend the HMM method by combining it with the DTW algorithm in order to combine the advantages of these two powerful pattern recognition technique. In t...

Dynamic Time Warping (DTW) is one of the basic similarity measures between curves or general temporal sequences (e.g., time series) that are represented as sequence of points in some metric space pX, distq. The DTW measure is massively used in many practical fields of computer science, and computing the DTW between two sequences is a classical problem in P. Despite extensive efforts to find mor...