AIDA: An Active Inference-Based Design Agent for Audio Processing Algorithms

  title={AIDA: An Active Inference-Based Design Agent for Audio Processing Algorithms},
  author={Albert Podusenko and Bart van Erp and Magnus T. Koudahl and Bert de Vries},
  booktitle={Frontiers in Signal Processing},
In this paper we present Active Inference-Based Design Agent (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… 

Reactive Message Passing for Scalable Bayesian Inference

It is shown that the RMP framework is able to run Bayesian inference for large-scale probabilistic state space models with hundreds of thousands of random variables on a standard laptop computer.



A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms

A fully probabilistic approach to “situated soundscaping” is developed, which aims at enabling users to make on-the-spot decisions about the enhancement or suppression of individual acoustic sources.

On Sequential Bayesian Optimization with Pairwise Comparison

The normalized weighted Kullback-Leibler (KL) divergence between true and agent-assigned predictive user response distributions as a metric to assess the quality of learned preferences in the problem of user preference learning is proposed.

Active Inference: Demystified and Compared

This letter aims to demystify the behavior of active inference agents by presenting an accessible discrete state-space and time formulation and demonstrating these behaviors in a OpenAI gym environment, alongside reinforcement learning agents.

A Bayesian Hierarchical Model for Blind Audio Source Separation

A fully Bayesian hierarchical model for blind audio source separation in a noisy environment based on Gaussian priors for the speech signals, Gamma hyperpriors forThe speech precisions and a Gamma prior for the noise precision is presented.

Scaling Active Inference

This work presents a working implementation of active inference that applies to high-dimensional tasks, with proof-of-principle results demonstrating efficient exploration and an order of magnitude increase in sample efficiency over strong model-free baselines.

Active inference on discrete state-spaces: A synthesis

Perception-Based Personalization of Hearing Aids Using Gaussian Processes and Active Learning

An interactive hearing-aid personalization system is proposed that obtains an optimal individual setting of the hearing aids from direct perceptual user feedback and may have potential for clinical usage to assist both the hearing-care professional and the user.

Simulating Active Inference Processes by Message Passing

This work describes AI agents in a dynamic environment as probabilistic state space models (SSM) and performs inference for perception and control in these agents by message passing on a factor graph representation of the SSM and proposes a formal experimental protocol for simulated AI.

Message Passing-Based Inference for Time-Varying Autoregressive Models

This paper represents the TVAR model by a factor graph and solves the inference problem by automated message passing-based inference for states and parameters by derive structured variational update rules for a composite “AR node” with probabilistic observations that can be used as a plug-in module in hierarchical models.