Metric Learning for Temporal Sequence Alignment
Authors: Damien Garreau, Rémi Lajugie, Sylvain Arlot, Francis Bach
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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 damien.garreau@ens.fr Remi Lajugie INRIA remi.lajugie@inria.fr Sylvain Arlot CNRS sylvain.arlot@ens.fr Francis Bach INRIA francis.bach@inria.fr |
| 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. |