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.
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