# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 4 Discussion  
From Figures 9 and 10 it can be noted that the proposed soundscaping framework is capable of achieving increased speech quality for the current models. The performance metrics are inherently tied to the soundscaping weights \(w\). Making adjustments to these weights can significantly increase the PESQ and STOI scores above the baseline performance. However, it should be noted that the PESQ and STOI metrics are by definition  
Figure 10: Overview of the performance metrics for the second experiment as described in Section 3.1. For a varying input SNR, the output SNR (**left**), the PESQ (**middle**) and the STOI (**right**) are evaluated for both models from Section 2.2 for 2 noise mixture components. For comparison the baseline performance of the noisy signal has been included, in addition to the results obtained by the Wiener filter. It should be noted that the input SNR refers to the signal before entering the frequency-warped filter, causing the offset in the left plot. Figure 9: Overview of the performance metrics for the first experiment as described in Section 3.1. For a varying input SNR, the output SNR (**left**), the PESQ (**middle**) and the STOI (**right**) are evaluated for both models from Section 2.2 for 1 noise mixture component. For comparison the baseline performance of the noisy signal has been included, in addition to the results obtained by the Wiener filter. It should be noted that the input SNR refers to the signal before entering the frequency-warped filter, causing the offset in the left plot. average group metrics. Therefore we expect that personalization of the weights \(w\) will lead to better perceived speech quality scores. This paper lies at the foundation of a novel class of personalized and situated hearing aid algorithms. In the current framework the parameter estimation approach and the signal processing algorithm follow naturally from minimizing the variational free energy through probabilistic inference in a generative model. The framework allows hearing aid users to develop their own algorithms according to their preferences. Therefore it will ease the burden on hearing aid users as they do not need the help of specialists to personalize their hearing experience. This will greatly shorten the personalization procedure for users and is likely to yield more satisfying results. Although the main application of this paper concerned hearing aids, its generality extends to a broader class of applications. The framework is a thorough specification of the principle of informed source separation [6]. Therefore its usage extends to any source separation problem where information about the underlying sources is known or can be acquired, such as in denoising problems in biomedical signal processing or communication theory. Future steps include the incorporation of user feedback and the learning of the acoustic model structure. Both improvements can be based on the same principle of free energy minimization, whose research fields are known as active inference [59] and Bayesian model reduction [60; 61], respectively. The active inference approach to preference learning has the goal of imposing as little burden on the user as possible. We envision the user giving feedback to the algorithm through an accompanied app, which will be used for optimizing the source-specific gains depending on the acoustic context. Through Bayesian model reduction, the algorithm will automatically learn the optimal pruned model structure from a very generic probabilistic model by optimizing the free energy, which equals the simultaneous optimization of both the model accuracy and model complexity. This last step is required to bring down the computational complexity as the current implementation for the specified model is not yet suitable for real-time usage in hearing aid devices. This is a result of the inherent complexity of mixture models and the variational approximations which require multiple iterations for convergence.