  In Psychology, actions are paramount for humans to identify sound events. In
Machine Learning (ML), action recognition achieves high accuracy; however, it
has not been asked whether identifying actions can benefit Sound Event
Classification (SEC), as opposed to mapping the audio directly to a sound
event. Therefore, we propose a new Psychology-inspired approach for SEC that
includes identification of actions via human listeners. To achieve this goal,
we used crowdsourcing to have listeners identify 20 actions that in isolation
or in combination may have produced any of the 50 sound events in the
well-studied dataset ESC-50. The resulting annotations for each audio recording
relate actions to a database of sound events for the first time. The
annotations were used to create semantic representations called Action Vectors
(AVs). We evaluated SEC by comparing the AVs with two types of audio features
-- log-mel spectrograms and state-of-the-art audio embeddings. Because audio
features and AVs capture different abstractions of the acoustic content, we
combined them and achieved one of the highest reported accuracies (88%).
