  Foundation models (FMs) are increasingly spearheading recent advances on a
variety of tasks that fall under the purview of computer audition -- the use of
machines to understand sounds. They feature several advantages over traditional
pipelines: among others, the ability to consolidate multiple tasks in a single
model, the option to leverage knowledge from other modalities, and the
readily-available interaction with human users. Naturally, these promises have
created substantial excitement in the audio community, and have led to a wave
of early attempts to build new, general-purpose foundation models for audio. In
the present contribution, we give an overview of computational audio analysis
as it transitions from traditional pipelines towards auditory foundation
models. Our work highlights the key operating principles that underpin those
models, and showcases how they can accommodate multiple tasks that the audio
community previously tackled separately.
