Dynamic time warping

About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.

An approach to speaker identification using multiple classifiers

Abstract: The presented paper is interested in a speaker identification problem The attributes representing the voice of a particular speaker are obtained from very short segments of the speech waveform corresponding only to one pitch period of vowels The patterns formed from the samples of a pitch period waveform are either matched in the time domain by use of a nonlinear time warping method, known as dynamic time warping (DTW), or they are converted into cepstral coefficients and compared using the cepstral distance measure Since an uttered speech signal usually contains a lot of vowels the techniques using a combination both various classifiers and multiple classifier outputs are considered in the decision making process Experiments performed for a hundred speakers are described

Similarity measure based on piecewise linear approximation and derivative dynamic time warping for time series mining

Abstract: We propose a new method to calculate the similarity of time series based on piecewise linear approximation (PLA) and derivative dynamic time warping (DDTW). The proposed method includes two phases. One is the divisive approach of piecewise linear approximation based on the middle curve of original time series. Apart from the attractive results, it can create line segments to approximate time series faster than conventional linear approximation. Meanwhile, high dimensional space can be reduced into a lower one and the line segments approximating the time series are used to calculate the similarity. In the other phase, we utilize the main idea of DDTW to provide another similarity measure based on the line segments just we got from the first phase. We empirically compare our new approach to other techniques and demonstrate its superiority.

Extraction and Classification of Multichannel Electromyographic Activation Trajectories for Hand Movement Recognition

Abstract: This paper proposes a system for hand movement recognition using multichannel electromyographic (EMG) signals obtained from the forearm surface. This system can be used to control prostheses or to provide inputs for a wide range of human computer interface systems. In this work, the hand movement recognition problem is formulated as a multi-class distance based classification of multi-dimensional sequences. More specifically, the extraction of multi-channel EMG activation trajectories underlying hand movements, and classifying the extracted trajectories using a metric based on multi-dimensional dynamic time warping are investigated. The developed methods are evaluated using the publicly available NINAPro database comprised of 40 different hand movements performed by 40 subjects. The average movement error rate obtained across the 40 subjects is $0.09\pm 0.047$ . The low error rate demonstrates the efficacy of the proposed trajectory extraction method and the discriminability of the utilized distance metric.

A comparison of methods to estimate seismic phase delays: numerical examples for coda wave interferometry

Abstract: Time-shift estimation between arrivals in two seismic traces before and after a velocity perturbation is a crucial step in many seismic methods. The accuracy of the estimated velocity perturbation location and amplitude depend on this time shift. Windowed cross-correlation and trace stretching are two techniques commonly used to estimate local time shifts in seismic signals. In the work presented here we implement Dynamic Time Warping (DTW) to estimate the warping function – a vector of local time shifts that globally minimizes the misfit between two seismic traces. We compare all three methods using acoustic numerical experiments. We show that DTW is comparable to or better than the other two methods when the velocity perturbation is homogeneous and the signal-to-noise ratio is high. When the signal-to-noise ratio is low, we find that DTW and windowed cross-correlation are more accurate than the stretching method. Finally, we show that the DTW algorithm has good time resolution when identifying small differences in the seismic traces for a model with an isolated velocity perturbation. These results impact current methods that utilize not only time shifts between (multiply) scattered waves, but also amplitude and decoherence measurements.