Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Metric Learning for Temporal Sequence Alignment
Authors: Damien Garreau, Rémi Lajugie, Sylvain Arlot, Francis Bach
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | Damien Garreau ENS EMAIL Remi Lajugie INRIA EMAIL Sylvain Arlot CNRS EMAIL Francis Bach INRIA EMAIL |
| Pseudocode | Yes | This algorithm is described in Alg. 1 of the supplementary material. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | First, we applied our method on the dataset of Kirchhoff and Lerch [14]. In this dataset, pairs of aligned examples (Ai, Bi) are artificially created by stretching an original audio signal. That way, the groundtruth alignment Y i is known and thus the data falls into our setting A more precise description of the dataset can be found in [14]. The Bach 10 dataset3 consists in ten J. S. Bach s Chorales (small quadriphonic pieces). http://music.cs.northwestern.edu/data/Bach10.html. |
| Dataset Splits | No | The paper mentions 'n pairs of training instances' but does not specify exact dataset split percentages, sample counts, or cross-validation setup for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (like exact GPU/CPU models or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper does not contain specific experimental setup details such as concrete hyperparameter values or comprehensive training configurations. |