A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms

The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community. As a result, interest in probabilistic programming frameworks has much increased over the past few years. This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms. To this end, we developed "ForneyLab" (https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message passing-based inference in FFGs. We show by example how ForneyLab enables automatic derivation of Bayesian signal processing algorithms, including algorithms for parameter estimation and model comparison. Crucially, due to the modular makeup of the FFG framework, both the model specification and inference methods are readily extensible in ForneyLab. In order to test this framework, we compared variational message passing as implemented by ForneyLab with automatic differentiation variational inference (ADVI) and Monte Carlo methods as implemented by state-of-the-art tools "Edward" and "Stan". In terms of performance, extensibility and stability issues, ForneyLab appears to enjoy an edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate Reasonin

Sound signal modelling based on recorded object sound

Disclosed is a hearing device, an electronic device and a method for modelling a sound signal in a hearing device. The hearing device is configured to be worn by a user. The hearing device comprises a first input transducer for providing an input signal. The hearing device comprises a first processing unit configured for processing the input signal according to a first sound signal model. The hearing device comprises an acoustic output transducer coupled to an output of the first processing unit for conversion of an output signal from the first processing unit into an audio output signal. The method comprises recording a first object signal by a recording unit. The recording is initiated by the user of the hearing device. The method comprises determining, by a second processing unit, a first set of parameter values of a second sound signal model for the first object signal. The method comprises subsequently receiving, in the first processing unit of the hearing device, an input signal comprising a first signal part, corresponding at least partly to the first object signal, and a second signal part. The method comprises applying the determined first set of parameter values of the second sound signal model to the first sound signal model. The method comprises processing the input signal according to the first sound signal model.</p

Simulating Active Inference Processes by Message Passing

The free energy principle (FEP) offers a variational calculus-based description for how biological agents persevere through interactions with their environment. Active inference (AI) is a corollary of the FEP, which states that biological agents act to fulfill prior beliefs about preferred future observations (target priors). Purposeful behavior then results from variational free energy minimization with respect to a generative model of the environment with included target priors. However, manual derivations for free energy minimizing algorithms on custom dynamic models can become tedious and error-prone. While probabilistic programming (PP) techniques enable automatic derivation of inference algorithms on free-form models, full automation of AI requires specialized tools for inference on dynamic models, together with the description of an experimental protocol that governs the interaction between the agent and its simulated environment. The contributions of the present paper are two-fold. Firstly, we illustrate how AI can be automated with the use of ForneyLab, a recent PP toolbox that specializes in variational inference on flexibly definable dynamic models. More specifically, we describe AI agents in a dynamic environment as probabilistic state space models (SSM) and perform inference for perception and control in these agents by message passing on a factor graph representation of the SSM. Secondly, we propose a formal experimental protocol for simulated AI. We exemplify how this protocol leads to goal-directed behavior for flexibly definable AI agents in two classical RL examples, namely the Bayesian thermostat and the mountain car parking problems

Sound signal modelling based on recorded object sound

Disclosed is a hearing device 2, an electronic device 46 and a method for modelling a sound signal in a hearing device. The hearing device comprises a first processing unit configured for processing the input signal according to a first sound signal model. The method comprises recording, initiated by the user, a first object signal 20 by a recording unit. A second processing unit determines a first set of parameter values of a second sound signal model for the first object signal. In the first processing unit of the hearing device, an input signal is then received, that comprises a first signal part, corresponding at least partly to the first object signal 20, and a second signal part. The determined first set of parameter values of the second sound signal model is applied to the first sound signal model and the input signal is processed according to the first sound signal model.</p

Realising Synthetic Active Inference Agents, Part II: Variational Message Updates

The Free Energy Principle (FEP) describes (biological) agents as minimising a variational Free Energy (FE) with respect to a generative model of their environment. Active Inference (AIF) is a corollary of the FEP that describes how agents explore and exploit their environment by minimising an expected FE objective. In two related papers, we describe a scalable, epistemic approach to synthetic AIF agents, by message passing on free-form Forney-style Factor Graphs (FFGs). A companion paper (part I) introduces a Constrained FFG (CFFG) notation that visually represents (generalised) FE objectives for AIF. The current paper (part II) derives message passing algorithms that minimise (generalised) FE objectives on a CFFG by variational calculus. A comparison between simulated Bethe and generalised FE agents illustrates how synthetic AIF induces epistemic behaviour on a T-maze navigation task. With a full message passing account of synthetic AIF agents, it becomes possible to derive and reuse message updates across models and move closer to industrial applications of synthetic AIF