Dynamic Time Warping
113 papers with code • 0 benchmarks • 0 datasets
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Use these libraries to find Dynamic Time Warping models and implementationsMost implemented papers
Data augmentation using synthetic data for time series classification with deep residual networks
hfawaz/aaltd18
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This is surprising as the accuracy of deep learning models for TSC could potentially be improved, especially for small datasets that exhibit overfitting, when a data augmentation method is adopted.
Multimodal One-Shot Learning of Speech and Images
rpeloff/multimodal-one-shot-learning
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Imagine a robot is shown new concepts visually together with spoken tags, e. g. "milk", "eggs", "butter".
Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping
This empirical study proposes two novel approaches for the early detection of sepsis: a deep learning model and a lazy learner based on time series distances.
Adversarial Attacks on Time Series
houshd/TS_Adv
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In this paper, we propose utilizing an adversarial transformation network (ATN) on a distilled model to attack various time series classification models.
A General Optimization Framework for Dynamic Time Warping
We pose the choice of warping function as an optimization problem with several terms in the objective.
Spatio-Temporal Alignments: Optimal transport through space and time
hichamjanati/spatio-temporal-alignements
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In this paper, we propose Spatio-Temporal Alignments (STA), a new differentiable formulation of DTW, in which spatial differences between time samples are accounted for using regularized optimal transport (OT).
Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis
We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets.
Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher
uchidalab/time_series_augmentation
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In order to address this problem, we propose a novel time series data augmentation called guided warping.
End-to-End Adversarial Text-to-Speech
yanggeng1995/EATS
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ICLR 2021
Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest.
Unsupervised Discovery of Recurring Speech Patterns Using Probabilistic Adaptive Metrics
One potential approach to this problem is to use dynamic time warping (DTW) to find well-aligning patterns from the speech data.
