  A data set of recorded single played tones of a concert grand piano is
investigated using Machine Learning (ML) on psychoacoustic timbre features. The
examined instrument has been recorded at two stages: firstly right after
manufacture and secondly after being played in a concert hall for one year. A
previous study [Plath2019] revealed that listeners clearly distinguished both
stages but no clear correlation with acoustics, signal processing tools or
verbalizations of perceived differences could be found. Using a Self-Organizing
Map (SOM), training single as well as double feature sets, it can be shown that
spectral flux is able to perfectly cluster the two stages. Sound Pressure Level
(SPL), roughness, and fractal correlation dimension (as a measure for initial
transient chaoticity) are furthermore able to order the keys with respect to
high and low notes. Combining spectral flux with the three other features in
double-feature training sets maintains stage clustering only for SPL and
fractal dimension, showing sub-clusters for both stages. These sub-clusters
point to a homogenization of SPL for stage 2 with respect to stage 1 and a
pronounced ordering and sub-clustering of key regions with respect to initial
transient chaoticity.
