Topic
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.
Papers published on a yearly basis
Papers
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TL;DR: A new formulation of PTW is described, working on peak-picked features rather than on complete profiles, which allows for a much more smooth integration in existing pipelines, and speeds up the algorithm by orders of magnitude.
Abstract: Alignment of peaks across samples is a difficult but unavoidable step in the data analysis for all analytical techniques containing a separation step like chromatography. Important application examples are the fields of metabolomics and proteomics. Parametric time warping (PTW) has already shown to be very useful in these fields because of the highly restricted form of the warping functions, avoiding overfitting. Here, we describe a new formulation of PTW, working on peak-picked features rather than on complete profiles. Not only does this allow for a much more smooth integration in existing pipelines, it also speeds up the (already among the fastest) algorithm by orders of magnitude. Using two publicly available datasets we show the potential of the new approach. The first set is a LC–DAD dataset of grape samples, and the second an LC–MS dataset of apple extracts
31 citations
TL;DR: The results show that the use of dimension reduction techniques on the extracted time series can improve the classification performance and the best recognition rate can be above 90%.
31 citations
TL;DR: This paper proposes a filter method to select a subset of time series using an adaptation of existing nonparametric mutual information estimators based on the k-nearest neighbor and relies on the use of dynamic time warping dissimilarity to bring these methods to the time series scenario.
31 citations
18 Sep 2006
TL;DR: An algorithm for discovering variable length patterns in real-valued time series that does not first discretize the data, runs in linear time, and requires constant memory by sampling the data stream rather than processing all of the data.
Abstract: This paper describes an algorithm for discovering variable length patterns in real-valued time series. In contrast to most existing pattern discovery algorithms, ours does not first discretize the data, runs in linear time, and requires constant memory. These properties are obtained by sampling the data stream rather than processing all of the data. Empirical results show that the algorithm performs well on both synthetic and real data when compared to an exhaustive algorithm.
31 citations
TL;DR: It is experimentally shown that one can optimize the system and further improve recognition accuracy for speaker-independent recognition by controlling the distance measure's sensitivity to spectral peaks and the spectral tilt and by utilizing the speech dynamic features.
Abstract: Several recently proposed automatic speech recognition (ASR) front-ends are experimentally compared in speaker-dependent, speaker-independent (or cross-speaker) recognition. The perceptually based linear predictive (PLP) front-end, with the root-power sums (RPS) distance measure, yields generally the highest accuracies, especially in cross-speaker recognition., It is experimentally shown that one can optimize the system and further improve recognition accuracy for speaker-independent recognition by controlling the distance measure's sensitivity to spectral peaks and the spectral tilt and by utilizing the speech dynamic features. For a digit vocabulary and five reference templates obtained with a clustering algorithm, the optimization improves recognition accuracy from 97% to 98.1%, with respect to the PL-PRPS front-end. >
31 citations