  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 use the scenario of the recent CORSMAL Containers Manipulation dataset and
consider two actions (shaking and pouring), and seven combinations of material
and filling level. The first step identifies the action performed by a person
with the container. The second step determines the amount and type of content
using an action-specific classifier. 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 the content amount and type either independently or jointly.
