Reflexive and Reflective Auditory Feedback

TL;DR: In this paper, a distinction is made between reflexive and reflective feedback mechanisms, the latter including cognitive activities, and a number of such feedback loops are described and discussed that have already been implemented and evaluated.

Abstract: Current models of binaural hearing go beyond bottom-up-driven processing and, instead, use a hybrid approach by including top-down, hypothesis-driven algorithms. Such hybrid models first identify and characterize auditory objects. Out of these objects, the model infers an auditory scene, from which it can extrapolate understanding, form judgments, and initiate actions. For example, when embedded in a mobile robot, a binaural hearing system can provide the information needed to carry out search-and-rescue tasks. Further, such systems are able to make judgments, for instance, on the quality of experience in spaces for musical performances. As with humans, such actions and judgments are based on sets of references built from perceptual structures, inherent and acquired knowledge, and the intellectual capabilities of the systems—in other words, on the “brains” of the model systems and the knowledge contained in them. To achieve these goals, adequate feedback loops must to be considered, evaluated, and implemented within technological models of auditory systems. In this chapter, a number of such feedback loops are described and discussed that have already been implemented and evaluated. A distinction is made between reflexive and reflective feedback mechanisms, the latter, including cognitive activities.