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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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Journal ArticleDOI
TL;DR: A hybrid gas detection algorithm based on an extreme random tree to greatly improve the classification accuracy and time efficiency is proposed and the dynamic time warping algorithm (DTW) is used to perform data pre-processing.
Abstract: Because of the low accuracy of the current machine olfactory algorithms in detecting two mixed gases, this study proposes a hybrid gas detection algorithm based on an extreme random tree to greatly improve the classification accuracy and time efficiency. The method mainly uses the dynamic time warping algorithm (DTW) to perform data pre-processing and then extracts the gas characteristics from gas signals at different concentrations by applying a principal component analysis (PCA). Finally, the model is established by using a new extreme random tree algorithm to achieve the target gas classification. The sample data collected by the experiment was verified by comparison experiments with the proposed algorithm. The analysis results show that the proposed DTW algorithm improves the gas classification accuracy by 26.87%. Compared with the random forest algorithm, extreme gradient boosting (XGBoost) algorithm and gradient boosting decision tree (GBDT) algorithm, the accuracy rate increased by 4.53%, 5.11% and 8.10%, respectively, reaching 99.28%. In terms of the time efficiency of the algorithms, the actual runtime of the extreme random tree algorithm is 66.85%, 90.27%, and 81.61% lower than that of the random forest algorithm, XGBoost algorithm, and GBDT algorithm, respectively, reaching 103.2568 s.

31 citations

Book ChapterDOI
01 Apr 2008
TL;DR: A Dynamic Time Warping based method is presented that outperforms the classical descriptors, being also invariant to scale, rotation, and elastic deformations typical found in handwriting musical notation.
Abstract: A growing interest in the document analysis field is the recognition of old handwritten documents, towards the conversion into a readable format. The difficulties when we work with old documents are increased, and other techniques are required for recognizing handwritten graphical symbols that are drawn in such these documents. In this paper we present a Dynamic Time Warping based method that outperforms the classical descriptors, being also invariant to scale, rotation, and elastic deformations typical found in handwriting musical notation.

31 citations

Patent
Andre Heilper1, Dmitry Markman1
23 Mar 2005
TL;DR: In this paper, a distance matrix is computed by applying successive, incremental shifts to the rows or columns so as to produce a reshaped matrix, and a best-score path is calculated using vector operations to quantify a similarity between the first and second data sequences.
Abstract: A method for comparing data sequences includes accepting first and second data sequences of data elements. A distance matrix is computed. The matrix includes rows and columns of matrix elements, describing distances between the data elements of the first sequence and the data elements of the second data sequence. The distance matrix is reshaped by applying successive, incremental shifts to the rows or columns so as to produce a reshaped matrix. A best-score path through the reshaped matrix is calculated using vector operations, so as to quantify a similarity between the first and second data sequences. Due to vectorization, a significant increase in computation speed is achieved in both software and hardware implementations.

31 citations

Journal ArticleDOI
TL;DR: The main purpose of this paper is to learn the control performance of an expert by imitating the demonstrations of a multirotor UAV (unmanned aerial vehicle) operated by an expert pilot using inverse reinforcement learning.
Abstract: The main purpose of this paper is to learn the control performance of an expert by imitating the demonstrations of a multirotor UAV (unmanned aerial vehicle) operated by an expert pilot. First, we collect a set of several demonstrations by an expert for a certain task which we want to learn. We extract a representative trajectory from the dataset. Here, the representative trajectory includes a sequence of state and input. The trajectory is obtained using hidden Markov model (HMM) and dynamic time warping (DTW). In the next step, the multirotor learns to track the trajectory for imitation. Although we have data of feed-forward input for each time sequence, using this input directly can deteriorate the stability of the multirotor due to insufficient data for generalization and numerical issues. For that reason, a controller is needed which generates the input command for the suitable flight maneuver. To design such a controller, we learn the hidden reward function of a quadratic form from the demonstrated flights using inverse reinforcement learning. After we find the optimal reward function that minimizes the trajectory tracking error, we design a reinforcement learning based controller using this reward function. The simulation and experiment applied to a multirotor UAV show successful imitation results.

31 citations

Journal ArticleDOI
TL;DR: A computational architecture to discover and collect occurrences of speech repetitions, or motifs, in a totally unsupervised fashion, that is in the absence of acoustic, lexical or pronunciation modeling and training material is described and evaluated.
Abstract: This paper describes and evaluates a computational architecture to discover and collect occurrences of speech repetitions, or motifs, in a totally unsupervised fashion, that is in the absence of acoustic, lexical or pronunciation modeling and training material. In the last few years, this task has known an increasing interest from the speech community because of a) its potential applicability in spoken document processing (as a preliminary step to summarization, topic clustering, etc.) and b) its novel methodology, that defines a new paradigm to speech processing that circumvents the issues common to all supervised, trained technologies. The contributions implied by the proposed system are two-fold: 1) the design of a discovery strategy that detects repetitions by extending matches of motif fragments, called seeds; 2) the implementation of template matching techniques to detect acoustically close segments, based on dynamic time warping (DTW) and self-similarity matrix (SSM) comparison of speech templates, in contrast to the decoding procedures of model-based recognition systems. The architecture is thoroughly evaluated on several hours of French broadcast news shows according to various parameter settings and acoustic features, namely mel-frequency cepstral coefficients (MFCCs) and different types of posteriorgrams: Gaussian mixture model (GMM)-based, and phone-based posteriors, in both language-matched and mismatched conditions. The evaluation highlights a) the improved robustness of the system that jointly employs DTW and SSM and b) the relevant impact of language-specific features to acoustic similarity detection based on template matching.

31 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023236
2022471
2021341
2020416
2019420
2018377