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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TL;DR: The system demonstration led to promising results with respect to the accuracy of waste level estimation (98.50%) and the application can be used to optimize the routing of waste collection based on the estimated bin level.
59 citations
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27 May 2007TL;DR: This work demonstrates that proposed methods to find the real average of the time series cannot produce the realaverage, and suggests a method to potentially find the shape-based time series average.
Abstract: Shape averaging or signal averaging of time series data is one of the prevalent subroutines in data mining tasks, where Dynamic Time Warping distance measure (DTW) is known to work exceptionally well with these time series data, and has long been demonstrated in various data mining tasks involving shape similarity among various domains. Therefore, DTW has been used to find the averageshape of two time series according to the optimal mapping between them. Several methods have been proposed, some of which require the number of time series being averaged to be a power of two. In this work, we will demonstrate that these proposed methods cannot produce the realaverage of the time series. We conclude with a suggestion of a method to potentially find the shape-based time series average.
59 citations
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21 Apr 1997TL;DR: It is shown that significant advantage can be gained by performing frequency warping and ML speaker adaptation in a unified framework and a procedure is described which compensates utterances by simultaneously scaling the frequency axis and reshaping the spectral energy contour.
Abstract: Frequency warping approaches to speaker normalization have been proposed and evaluated on various speech recognition tasks. These techniques have been found to significantly improve performance even for speaker independent recognition from short utterances over the telephone network. In maximum likelihood (ML) based model adaptation a linear transformation is estimated and applied to the model parameters in order to increase the likelihood of the input utterance. The purpose of this paper is to demonstrate that significant advantage can be gained by performing frequency warping and ML speaker adaptation in a unified framework. A procedure is described which compensates utterances by simultaneously scaling the frequency axis and reshaping the spectral energy contour. This procedure is shown to reduce the error rate in a telephone based connected digit recognition task by 30-40%.
59 citations
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24 May 2011TL;DR: A novel instance selection method that exploits the hubness phenomenon in time-series data, which states that some few instances tend to be much more frequently nearest neighbors compared to the remaining instances is introduced.
Abstract: Time-series classification is a widely examined data mining task with various scientific and industrial applications. Recent research in this domain has shown that the simple nearest-neighbor classifier using Dynamic Time Warping (DTW) as distance measure performs exceptionally well, in most cases outperforming more advanced classification algorithms. Instance selection is a commonly applied approach for improving efficiency of nearest-neighbor classifier with respect to classification time. This approach reduces the size of the training set by selecting the best representative instances and use only them during classification of new instances. In this paper, we introduce a novel instance selection method that exploits the hubness phenomenon in time-series data, which states that some few instances tend to be much more frequently nearest neighbors compared to the remaining instances. Based on hubness, we propose a framework for score-based instance selection, which is combined with a principled approach of selecting instances that optimize the coverage of training data. We discuss the theoretical considerations of casting the instance selection problem as a graph-coverage problem and analyze the resulting complexity. We experimentally compare the proposed method, denoted as INSIGHT, against FastAWARD, a state-of-the-art instance selection method for time series. Our results indicate substantial improvements in terms of classification accuracy and drastic reduction (orders of magnitude) in execution times.
59 citations
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TL;DR: In this paper, a model adaptation system for speaker verification is presented, which can be applied to several types of speaker recognition models including neural tree networks, Gaussian mixture models, and dynamic time warping (DTW).
Abstract: The model adaptation system of the present invention is a speaker verification system that embodies the capability to adapt models learned during the enrollment component to track aging of a user's voice. The system has the advantage of only requiring a single enrollment for the user. The model adaptation system and methods can be applied to several types of speaker recognition models including neural tree networks (NTN), Gaussian Mixture Models (GMMs), and dynamic time warping (DTW) or to multiple models (i.e., combinations of NTNs, GMMs and DTW). Moreover, the present invention can be applied to text-dependent or text-independent systems.
59 citations