Dynamic Time Warping
96 papers with code • 0 benchmarks • 0 datasets
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Most implemented papers
Soft-DTW: a Differentiable Loss Function for Time-Series
We propose in this paper a differentiable learning loss between time series, building upon the celebrated dynamic time warping (DTW) discrepancy.
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models
We introduce a differentiable loss function suitable for training deep neural nets, and provide a custom back-prop implementation for speeding up optimization.
Learning the Beauty in Songs: Neural Singing Voice Beautifier
Furthermore, we propose a latent-mapping algorithm in the latent space to convert the amateur vocal tone to the professional one.
A user-driven case-based reasoning tool for infilling missing values in daily mean river flow records
In this work, we introduce gapIt, a user-driven case-based reasoning tool for infilling gaps in daily mean river flow records.
Multi-Scale Convolutional Neural Networks for Time Series Classification
These methods are ad-hoc and separate the feature extraction part with the classification part, which limits their accuracy performance.
shapeDTW: shape Dynamic Time Warping
Dynamic Time Warping (DTW) is an algorithm to align temporal sequences with possible local non-linear distortions, and has been widely applied to audio, video and graphics data alignments.
Times series averaging and denoising from a probabilistic perspective on time-elastic kernels
In the light of regularized dynamic time warping kernels, this paper re-considers the concept of time elastic centroid for a setof time series.
TimeNet: Pre-trained deep recurrent neural network for time series classification
Inspired by the tremendous success of deep Convolutional Neural Networks as generic feature extractors for images, we propose TimeNet: a deep recurrent neural network (RNN) trained on diverse time series in an unsupervised manner using sequence to sequence (seq2seq) models to extract features from time series.
Human Motion Analysis with Deep Metric Learning
Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks.
Data augmentation using synthetic data for time series classification with deep residual networks
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.