  In this paper we present AIDA, which is an active inference-based agent that
iteratively designs a personalized audio processing algorithm through situated
interactions with a human client. The target application of AIDA is to propose
on-the-spot the most interesting alternative values for the tuning parameters
of a hearing aid (HA) algorithm, whenever a HA client is not satisfied with
their HA performance. AIDA interprets searching for the "most interesting
alternative" as an issue of optimal (acoustic) context-aware Bayesian trial
design. In computational terms, AIDA is realized as an active inference-based
agent with an Expected Free Energy criterion for trial design. This type of
architecture is inspired by neuro-economic models on efficient (Bayesian) trial
design in brains and implies that AIDA comprises generative probabilistic
models for acoustic signals and user responses. We propose a novel generative
model for acoustic signals as a sum of time-varying auto-regressive filters and
a user response model based on a Gaussian Process Classifier. The full AIDA
agent has been implemented in a factor graph for the generative model and all
tasks (parameter learning, acoustic context classification, trial design, etc.)
are realized by variational message passing on the factor graph. All
verification and validation experiments and demonstrations are freely
accessible at our GitHub repository.
