  In Psychology, actions are paramount for humans to perceive and separate
sound events. In Machine Learning (ML), action recognition achieves high
accuracy; however, it has not been asked if 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~\footnote{Annotations will be released after revision.}. 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 (86.75%) in ESC-50, showing
that Psychology-inspired approaches can improve SEC.
