AES Europe 2026

String ageing is a familiar; perceptually important
phenomenon for guitarists; players of other stringed
instruments. From the moment a new set of strings is
installed, the sound they produce when excited begins to
change due to a combination of chemical degradation,
corrosion,; mechanical wear arising from playing.
Musicians commonly report that aged strings sound dull,
lack sustain,; feel less responsive compared to new
strings. String ageing is a function of both elapsed time
; accumulated playing time, with repeated playing
accelerating degradation through contamination; repeated
mechanical stress.

Previous studies have investigated individual aspects of
string ageing by artificially accelerating wear;
performing controlled acoustic measurements, identifying
effects such as increased damping of higher partials;
increased inharmonicity. While these approaches provide
valuable physical insight, the tightly constrained
experimental conditions differ significantly from
real-world playing conditions.

This paper presents a dataset of audio recordings of guitar
playing over a four-week period, starting from the point of
new strings being installed.
Audio performance data from different sets of electric
guitar strings is recorded daily over a four-week period,
using strictly fixed musical exercises that are repeated
multiple times per session. By collecting many takes of
identical material at each stage of string age, the dataset
enables statistical analysis of ageing-related changes
while accounting for natural performance variability.

The dataset is intended to support exploratory machine
learning investigations into string ageing, including
questions of how ageing manifests over time; playing
duration, whether string age can be predicted from audio
alone,; which audio features or learned representations
capture perceptually relevant aspects of the ageing process.