Next Article in Journal
Detection of Pediatric Femur Configuration on X-ray Images
Next Article in Special Issue
Attention-Based CNN and Bi-LSTM Model Based on TF-IDF and GloVe Word Embedding for Sentiment Analysis
Previous Article in Journal
Use of 2SFCA Method to Identify and Analyze Spatial Access Disparities to Healthcare in Jeddah, Saudi Arabia
Previous Article in Special Issue
Preprocessing for Unintended Conducted Emissions Classification with ResNet
Article

A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms

1
Department of Electrical Engineering, Eindhoven University of Technology, 5612 AP Eindhoven, The Netherlands
2
GN Hearing, 5612 AB Eindhoven, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editors: Jongweon Kim and Yongseok Lee
Appl. Sci. 2021, 11(20), 9535; https://doi.org/10.3390/app11209535
Received: 13 August 2021 / Revised: 5 October 2021 / Accepted: 8 October 2021 / Published: 14 October 2021
(This article belongs to the Special Issue AI, Machine Learning and Deep Learning in Signal Processing)
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 or 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. View Full-Text
Keywords: Bayesian machine learning; factor graphs; noise reduction; situated soundscaping; speech enhancement; variational message passing Bayesian machine learning; factor graphs; noise reduction; situated soundscaping; speech enhancement; variational message passing
Show Figures

Figure 1

MDPI and ACS Style

van Erp, B.; Podusenko, A.; Ignatenko, T.; de Vries, B. A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms. Appl. Sci. 2021, 11, 9535. https://doi.org/10.3390/app11209535

AMA Style

van Erp B, Podusenko A, Ignatenko T, de Vries B. A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms. Applied Sciences. 2021; 11(20):9535. https://doi.org/10.3390/app11209535

Chicago/Turabian Style

van Erp, Bart, Albert Podusenko, Tanya Ignatenko, and Bert de Vries. 2021. "A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms" Applied Sciences 11, no. 20: 9535. https://doi.org/10.3390/app11209535

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop