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

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

Variational message passing for online polynomial NARMAX identification

We propose a variational Bayesian inference procedure for online nonlinear system identification. For each output observation, a set of parameter posterior distributions is updated, which is then used to form a posterior predictive distribution for future outputs. We focus on the class of polynomial NARMAX models, which we cast into probabilistic form and represent in terms of a Forney-style factor graph. Inference in this graph is efficiently performed by a variational message passing algorithm. We show empirically that our variational Bayesian estimator outperforms an online recursive least-squares estimator, most notably in small sample size settings and low noise regimes, and performs on par with an iterative least-squares estimator trained offline.Comment: 6 pages, 4 figures. Accepted to the American Control Conference 202

RxInfer: A Julia package for reactive real-time Bayesian inference

RxInfer v2.12.0 Diff since v2.11.3 Merged pull requests: Modify inference for predictions functionality (#51) (@albertpod) Move pkgeval badge to the developers documentation (#146) (@bvdmitri) Update README.md (#148) (@bvdmitri) Closed issues: Predictive posterior distributions (#58)If you use this software, please cite our article in the Journal of Open Source Software

Comparative Analysis of Classifiers for Classification of Emergency Braking of Road Motor Vehicles

We investigate the feasibility of classifying (inferring) the emergency braking situations in road vehicles from the motion pattern of the accelerator pedal. We trained and compared several classifiers and employed genetic algorithms to tune their associated hyperparameters. Using offline time series data of the dynamics of the accelerator pedal as the test set, the experimental results suggest that the evolved classifiers detect the emergency braking situation with at least 93% accuracy. The best performing classifier could be integrated into the agent that perceives the dynamics of the accelerator pedal in real time and—if emergency braking is detected—acts by applying full brakes well before the driver would have been able to apply them

Coevolution of the Features of the Dynamics of the Accelerator Pedal and Hyperparameters of the Classifier for Emergency Braking Detection

We investigate the feasibility of inferring the intention of the human driver of road motor vehicles to apply emergency braking solely by analyzing the dynamics of lifting the accelerator pedal. Focusing on building the system that reliably classifies the emergency braking situations, we employed evolutionary algorithms (EA) to coevolve both (i) the set of features that optimally characterize the movement of accelerator pedal and (ii) the values of the hyperparameters of the classifier. The experimental results demonstrate the superiority of the coevolutionary approach over the analogical approaches that rely on an a priori defined set of features and values of hyperparameters. By using simultaneous evolution of both features and hyperparameters, the learned classifier inferred the emergency braking situations in previously unforeseen dynamics of the accelerator pedal with an accuracy of about 95%. We consider the obtained results as a step towards the development of a brake-assisting system, which would perceive the dynamics of the accelerator pedal in a real-time and in case of a foreseen emergency braking situation, would apply the brakes automatically well before the human driver would have been able to apply them

Variational message passing for online polynomial NARMAX identification

We propose a variational Bayesian inference procedure for online nonlinear system identification. For each output observation, a set of parameter posterior distributions is updated, which is then used to form a posterior predictive distribution for future outputs. We focus on the class of polynomial NARMAX models, which we cast into probabilistic form and represent in terms of a Forney-style factor graph. Inference in this graph is efficiently performed by a variational message passing algorithm. We show empirically that our variational Bayesian estimator outperforms an online recursive least-squares estimator, most notably in small sample size settings and low noise regimes, and performs on par with an iterative least-squares estimator trained offline