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.
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Papers
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09 Jun 2013TL;DR: This paper proposes a new approach to automatic evolutionary selection of the dynamic signature global features, which was tested with use of the SVC2004 public on-line signature database.
Abstract: Methods using dynamic signature for identity verification may be divided into three main categories: global methods, local function based methods and regional function based methods. Global methods base on a set of global parametric features, which are extracted from signature of user. Global feature extraction methods have been often presented in the literature. Another interesting task is selection of a features group which will be considered individually for each user during training and verification process. In this paper we propose a new approach to automatic evolutionary selection of the dynamic signature global features. Our method was tested with use of the SVC2004 public on-line signature database.
43 citations
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25 Sep 2012TL;DR: The algorithm is described and the results indicate that ViSQOL is less prone to underestimation of speech quality in both scenarios than the ITU standard.
Abstract: A model of human speech quality perception has been developed to provide an objective measure for predicting subjective quality assessments. The Virtual Speech Quality Objective Listener (ViSQOL) model is a signal based full reference metric that uses a spectro-temporal measure of similarity between a reference and a test speech signal. This paper describes the algorithm and compares the results with PESQ for common problems in VoIP: clock drift, associated time warping and jitter. The results indicate that ViSQOL is less prone to underestimation of speech quality in both scenarios than the ITU standard.
43 citations
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TL;DR: By performing experiments on the entire UCR Time Series Classification Archive, it is shown that weighted kNN is able to consistently outperform 1NN and provides recommendations for the choices of the constraint width parameter r, neighborhood size k, and weighting scheme, for each mentioned elastic distance measure.
Abstract: Time-series classification has been addressed by a plethora of machine-learning techniques, including neural networks, support vector machines, Bayesian approaches, and others. It is an accepted fact, however, that the plain vanilla 1-nearest neighbor (1NN) classifier, combined with an elastic distance measure such as Dynamic Time Warping (DTW), is competitive and often superior to more complex classification methods, including the majority-voting k-nearest neighbor (kNN) classifier. With this paper we continue our investigation of the kNN classifier on time-series data and the impact of various classic distance-based vote weighting schemes by considering constrained versions of four common elastic distance measures: DTW, Longest Common Subsequence (LCS), Edit Distance with Real Penalty (ERP), and Edit Distance on Real sequence (EDR). By performing experiments on the entire UCR Time Series Classification Archive we show that weighted kNN is able to consistently outperform 1NN. Furthermore, we provide recommendations for the choices of the constraint width parameter r, neighborhood size k, and weighting scheme, for each mentioned elastic distance measure.
43 citations
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TL;DR: This study proposes a novel, data augmentation based forecasting framework that is capable of improving the baseline accuracy of the GFM models in less data-abundant settings and can outperform state-of-the-art univariate forecasting methods.
Abstract: Forecasting models that are trained across sets of many time series, known as Global Forecasting Models (GFM), have shown recently promising results in forecasting competitions and real-world applications, outperforming many state-of-the-art univariate forecasting techniques. In most cases, GFMs are implemented using deep neural networks, and in particular Recurrent Neural Networks (RNN), which require a sufficient amount of time series to estimate their numerous model parameters. However, many time series databases have only a limited number of time series. In this study, we propose a novel, data augmentation based forecasting framework that is capable of improving the baseline accuracy of the GFM models in less data-abundant settings. We use three time series augmentation techniques: GRATIS, moving block bootstrap (MBB), and dynamic time warping barycentric averaging (DBA) to synthetically generate a collection of time series. The knowledge acquired from these augmented time series is then transferred to the original dataset using two different approaches: the pooled approach and the transfer learning approach. When building GFMs, in the pooled approach, we train a model on the augmented time series alongside the original time series dataset, whereas in the transfer learning approach, we adapt a pre-trained model to the new dataset. In our evaluation on competition and real-world time series datasets, our proposed variants can significantly improve the baseline accuracy of GFM models and outperform state-of-the-art univariate forecasting methods.
43 citations
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26 Jan 2021TL;DR: In this paper, a deep learning approach named Time-Aligned Recurrent Neural Networks (TA-RNNs) was proposed for online handwritten signature verification, which combines the potential of dynamic time warping and recurrent neural networks to train robust systems against forgeries.
Abstract: Deep learning has become a breathtaking technology in the last years, overcoming traditional handcrafted approaches and even humans for many different tasks. However, in some tasks, such as the verification of handwritten signatures, the amount of publicly available data is scarce, what makes difficult to test the real limits of deep learning. In addition to the lack of public data, it is not easy to evaluate the improvements of novel proposed approaches as different databases and experimental protocols are usually considered. The main contributions of this study are: i) we provide an in-depth analysis of state-of-the-art deep learning approaches for on-line signature verification, ii) we present and describe the new DeepSignDB on-line handwritten signature biometric public database, 1 iii) we propose a standard experimental protocol and benchmark to be used for the research community in order to perform a fair comparison of novel approaches with the state of the art, and iv) we adapt and evaluate our recent deep learning approach named Time-Aligned Recurrent Neural Networks (TA-RNNs) 2 . for the task of on-line handwritten signature verification. This approach combines the potential of Dynamic Time Warping and Recurrent Neural Networks to train more robust systems against forgeries. Our proposed TA-RNN system outperforms the state of the art, achieving results even below 2.0% EER when considering skilled forgery impostors and just one training signature per user. 1 https://github.com/BiDAlab/DeepSignDB 2 Spanish Patent Application (P202030060).
42 citations