  Sounds are an important source of information on our daily interactions with
objects. For instance, a significant amount of people can discern the
temperature of water that it is being poured just by using the sense of
hearing. However, only a few works have explored the use of audio for the
classification of object interactions in conjunction with vision or as single
modality. In this preliminary work, we propose an audio model for egocentric
action recognition and explore its usefulness on the parts of the problem
(noun, verb, and action classification). Our model achieves a competitive
result in terms of verb classification (34.26% accuracy) on a standard
benchmark with respect to vision-based state of the art systems, using a
comparatively lighter architecture.
