A Longitudinal Dataset for Guitar String Ageing

Wright A, McKenzie T, Hamilton M. A Longitudinal Dataset for Guitar String Ageing. In: Express Paper. Audio Engineering Society; 2026. Paper 425. Available from: https://aes.org/publications/elibrary-page/?id=23154

@inproceedings{Wright2026_23154,
  author = {Wright, Alec and McKenzie, Thomas and Hamilton, Matthew},
  title = {{A Longitudinal Dataset for Guitar String Ageing}},
  note = {Paper 425},
  year = {2026},
  month = may,
  publisher = {Audio Engineering Society},
  url = {https://aes.org/publications/elibrary-page/?id=23154}
}

TY  - CPAPER
TI  - A Longitudinal Dataset for Guitar String Ageing
AU  - Wright, Alec
AU  - McKenzie, Thomas
AU  - Hamilton, Matthew
M1  - Paper 425
PY  - 2026
DA  - 2026/05/28
UR  - https://aes.org/publications/elibrary-page/?id=23154
PB  - Audio Engineering Society
LA  - en
AB  - This paper presents a longitudinal dataset of guitar audio recordings collected over a 28-day period performed by two performers. On day 1, a new set of strings was installed on both guitars. Every day, the same session of exercises was performed and recorded, yielding 11.5 minutes of audio data per day per guitarist. The session consists of 14 fixed musical segments performed at 60 BPM, with each segment a different playing exercise, from single note plucks and chromatic scales to strummed chord progressions. The repetitive design enables statistical analysis of ageing-related acoustic changes while accounting for natural performance variability. The dataset is intended to support machine learning investigations into string ageing and the development of audio effects for artificial string ageing or de-ageing. The dataset is made open-access and free to download on Zenodo.
ER  -