  Machine Listening, as usually formalized, attempts to perform a task that is,
from our perspective, fundamentally human-performable, and performed by humans.
Current automated models of Machine Listening vary from purely data-driven
approaches to approaches imitating human systems. In recent years, the most
promising approaches have been hybrid in that they have used data-driven
approaches informed by models of the perceptual, cognitive, and semantic
processes of the human system. Not only does the guidance provided by models of
human perception and domain knowledge enable better, and more generalizable
Machine Listening, in the converse, the lessons learned from these models may
be used to verify or improve our models of human perception themselves. This
paper summarizes advances in the development of such hybrid approaches, ranging
from Machine Listening models that are informed by models of peripheral (human)
auditory processes, to those that employ or derive semantic information encoded
in relations between sounds. The research described herein was presented in a
special session on "Synergy between human and machine approaches to sound/scene
recognition and processing" at the 2023 ICASSP meeting.
