  The correlation between the sharpness of loss minima and generalisation in
the context of deep neural networks has been subject to discussion for a long
time. Whilst mostly investigated in the context of selected benchmark data sets
in the area of computer vision, we explore this aspect for the acoustic scene
classification task of the DCASE2020 challenge data. Our analysis is based on
two-dimensional filter-normalised visualisations and a derived sharpness
measure. Our exploratory analysis shows that sharper minima tend to show better
generalisation than flat minima -even more so for out-of-domain data, recorded
from previously unseen devices-, thus adding to the dispute about better
generalisation capabilities of flat minima. We further find that, in
particular, the choice of optimisers is a main driver of the sharpness of
minima and we discuss resulting limitations with respect to comparability. Our
code, trained model states and loss landscape visualisations are publicly
available.
