  We address the problem of sound representation and classification and present
results of a comparative study in the context of a domestic robotic scenario. A
dataset of sounds was recorded in realistic conditions (background noise,
presence of several sound sources, reverberations, etc.) using the humanoid
robot NAO. An extended benchmark is carried out to test a variety of
representations combined with several classifiers. We provide results obtained
with the annotated dataset and we assess the methods quantitatively on the
basis of their classification scores, computation times and memory
requirements. The annotated dataset is publicly available at
https://team.inria.fr/perception/nard/.
