  Food containers, drinking glasses and cups handled by a person generate
sounds that vary with the type and amount of their content. In this paper, we
propose a new model for sound-based classification of the type and amount of
content in a container. The proposed model is based on the decomposition of the
problem into two steps, namely action recognition and content classification.
We consider the scenario of the recent CORSMAL Containers Manipulation dataset
and consider two actions (shaking and pouring), and seven material and filling
level combinations. The first step identifies the action a person performs
while manipulating a container. The second step is an appropriate classifier
trained for the specific interaction identified by the first step to classify
the amount and type of content. Experiments show that the proposed model
achieves 76.02, 78.24, and 41.89 weighted average F1 score on the three test
sets, respectively, and outperforms baselines and existing approaches that
classify either independently content level and content type or directly the
combination of content type and level together.
