# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
###### Abstract  
Effective noise reduction and speech enhancement algorithms have great potential to enhance lives of hearing aid users by restoring speech intelligibility. An open problem in today's commercial hearing aids is how to take into account users' preferences, indicating which acoustic sources should be suppressed or enhanced, since they are not only user-specific but also depend on many situational factors. In this paper, we develop a fully probabilistic approach to \"situated soundscaping\", which aims at enabling users to make on-the-spot (\"situated\") decisions about the enhancement of suppression of individual acoustic sources. The approach rests on a compact generative probabilistic model for acoustic signals. In this framework, all signal processing tasks (source modeling, source separation and soundscaping) are framed as automatable probabilistic inference tasks. These tasks can be efficiently executed using message passing-based inference on factor graphs. Since all signal processing tasks are automatable, the approach supports fast future model design cycles in an effort to reach commercializable performance levels. The presented results show promising performance in terms of SNR, PESQ and STOI improvements in a situated setting. Bayesian machine learning; factor graphs; noise reduction; situated soundscaping; speech enhancement; variational message passing +
Footnote †: 2021: 11, 9535. [[https://doi.org/10.3309/app11209535](https://doi.org/10.3309/app11209535)]([https://doi.org/10.3309/app11209535](https://doi.org/10.3309/app11209535))
source separation and allows users to fully personalize the algorithm under in-the-field conditions. The field of source separation can be subdivided into two groups. One research thread is based on _blind_ source separation methods (BSS) for acoustic signals [2; 3; 4; 5], which is commonly implemented by only assuming statistical independence between the different source signals and by optimizing for a selected independence metric. Unfortunately, the performance and computational costs of real-time BSS are not adequate for hearing aid applications. Rather we would like to help these algorithms in separating the sources by providing additional information of the sources in the mixture. In contrast to blind source separation, _informed_ source separation (ISS) methods use significant prior information about the observed signals [6]. ISS technology for audio signals typically use log-power domain models [7; 8; 9]. An issue with log-power domain models is that they contain an intractable signal mixing model [10] that is commonly approximated by the max-model [11] which leads to perceivable artifacts due to time-frequency masking [12]. Furthermore, this technique is commonly extended with non-negative matrix factorization (NMF) [13; 14; 15] for improved performance. On the interface of blind and informed source separation in a probabilistic context recently new works have been published. In [16] a blind signal separation algorithm is presented, based on earlier works in [17; 18; 19]. Here the individual signals are represented by state space models with a sparse input. Their approach allows for straightforward extensions and for the incorporation of prior model knowledge. As a consequence of the work on source separation, simultaneously significant effort has been invested in modeling acoustic signals, which lies at the heart of situated design. These research efforts have mainly been targeted at the probabilistic modeling of acoustic signals, see e.g., [7; 8; 12] and more recently at the modeling using \"deep\" neural networks (DNN) [20; 21; 22; 23; 24]. The latter field of research does not lend itself well to the situated hearing aid design application, due to high computational costs, time-consuming training procedures and large data set requirements.