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

arXiv: https://arxiv.org/abs/2112.13366 · HTML: https://mlsanity.com/paper/2112.13366/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 73 references that are in the map.
- First posted on arXiv 26 Dec 2021.

## Builds on (most cited first, 14 of 14)

- [Active inference on discrete state-spaces: a synthesis](https://mlsanity.com/paper/2001.07203/) (2020)
- [ViSQOL v3: An Open Source Production Ready Objective Speech and Audio Metric](https://mlsanity.com/paper/2004.09584/) (2020)
- [Loopy Belief Propagation for Approximate Inference: An Empirical Study](https://mlsanity.com/paper/1301.6725/) (2013)
- [Sophisticated Inference](https://mlsanity.com/paper/2006.04120/) (2020)
- [Deep Active Inference as Variational Policy Gradients](https://mlsanity.com/paper/1907.03876/) (2019)
- [Scaling active inference](https://mlsanity.com/paper/1911.10601/) (2019)
- [Bayesian model reduction](https://mlsanity.com/paper/1805.07092/) (2018)
- [Reactive Message Passing for Scalable Bayesian Inference](https://mlsanity.com/paper/2112.13251/) (2021)
- [A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms](https://mlsanity.com/paper/1811.03407/) (2018)
- [Personalization of Hearing Aid Compression by Human-In-Loop Deep Reinforcement Learning](https://mlsanity.com/paper/2007.00192/) (2020)
- [Application of the Free Energy Principle to Estimation and Control](https://mlsanity.com/paper/1910.09823/) (2019)
- [An individualized super Gaussian single microphone Speech Enhancement for hearing aid users with smartphone as an assistive device](https://mlsanity.com/paper/1812.03916/) (2018)
- [Informed Source Separation: A Bayesian Tutorial](https://mlsanity.com/paper/1311.3001/) (2013)
- [A Probabilistic Modeling Approach to Hearing Loss Compensation](https://mlsanity.com/paper/1602.01345/) (2016)

## Abstract (arXiv)

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.