# Metric Learning for Temporal Sequence Alignment

arXiv: https://arxiv.org/abs/1409.3136 · HTML: https://mlsanity.com/paper/1409.3136/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2014.

## Built on by (most cited first, 4 of 4)

- [Deep Declarative Dynamic Time Warping for End-to-End Learning of Alignment Paths](https://mlsanity.com/paper/2303.10778/) (2023)
- [Flat Latent Manifolds for Human-machine Co-creation of Music](https://mlsanity.com/paper/2202.12243/) (2022)
- [Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2506.20574/) (2025)
- [Clustering of timed sequences -- Application to the analysis of care pathways](https://mlsanity.com/paper/2404.15379/) (2024)

## Abstract (arXiv)

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 performance for the alignment.