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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.


Papers
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Journal ArticleDOI
TL;DR: The multi-template multi-match dynamic time warping (MTMM-DTW) algorithm is proposed as a natural extension of DTW to detect multiple occurrences of more than one exercise type in the recording of a physical therapy session and for providing feedback to the patient.

50 citations

Proceedings Article
08 Dec 2014
TL;DR: This paper proposes to learn a Mahalanobis distance to perform alignment of multivariate time series, and proposes to use this metric learning framework to perform feature selection and, from basic audio features, build a combination of these with better alignment performance.
Abstract: In this paper, we propose to learn a Mahalanobis distance to perform alignment of multivariate time series. The learning examples for this task are time series for which the true alignment is known. We cast the alignment problem as a structured prediction task, and propose realistic losses between alignments for which the optimization is tractable. We provide experiments on real data in the audio-to-audio context, where we show that the learning of a similarity measure leads to improvements in the performance of the alignment task. We also propose to use this metric learning framework to perform feature selection and, from basic audio features, build a combination of these with better alignment performance.

50 citations

Journal ArticleDOI
TL;DR: In this paper, the authors proposed and analyzed subgradient methods for the problem of finding a sample mean in DTW spaces, which generalizes existing sample mean algorithms such as DTW Barycenter Averaging (DBA).

50 citations

Journal ArticleDOI
TL;DR: In this work, machine learning methods are used on labelled databases of Hindustani and Carnatic vocal audio concerts to obtain phrase classification on manually segmented audio using Dynamic time warping and HMM based classification on time series of detected pitch values used for the melodic representation of a phrase.
Abstract: Ragas are characterized by their melodic motifs or catch phrases that constitute strong cues to the raga identity for both the performer and the listener, and therefore are of great interest in music retrieval and automatic transcription. While the characteristic phrases, or pakads, appear in written notation as a sequence of notes, musicological rules for interpretation of the phrase in performance in a manner that allows considerable creative expression, while not transgressing raga grammar, are not explicitly defined. In this work, machine learning methods are used on labelled databases of Hindustani and Carnatic vocal audio concerts to obtain phrase classification on manually segmented audio. Dynamic time warping and HMM based classification are applied on time series of detected pitch values used for the melodic representation of a phrase. Retrieval experiments on raga-characteristic phrases show promising results while providing interesting insights on the nature of variation in the surface ...

50 citations

Journal ArticleDOI
TL;DR: This paper introduces the human detection system for recognition of human gesture using a weighted dynamic time warping (DTW) with kinematic constraints, and proposes a feasible strategy to integrate these three aspects to achieve a conscious, safe, accurate, robust, and efficient navigation.
Abstract: Service robot navigation must take the humans into account explicitly so as to produce motion behaviors that reflect its social awareness. Generally, the navigation problems of mobile service robot can be summarized to three aspects: 1) human detection; 2) robot real-time localization; and 3) robot motion planning. The purpose of this paper is to provide a feasible strategy to integrate these three aspects to achieve a conscious, safe, accurate, robust, and efficient navigation. We first introduce the human detection system for recognition of human gesture using a weighted dynamic time warping (DTW) with kinematic constraints. Thus, by interpreting the human body language through gesture recognition, robot motion behaviors like heading to the assigned position or following people can be activated. Then, for the robot localization, a simultaneous localization and mapping (SLAM) method based on artificial and natural landmark recognition is employed to provide absolute position feedback in real time. For the motion planning, a novel quadrupole potential field (QPF) method is proposed to plan collision-free trajectories, adequately considering the nonholomic constraint of the mobile robot system. Then, a robust kinematic controller is designed for trajectory tracking to account for slip disturbances. Such a design automatically merges path finding, trajectory generation, and trajectory tracking in a closed-loop fashion, achieving simultaneous motion planning for obstacle avoidance and feedback stabilization to a desired position and orientation even in the presence of slippage. Finally, experiments prove the effectiveness and feasibility of the proposed strategy, showing a good navigation performance on mobile service robot.

50 citations


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