Audio | BIASlab

FEP-Lab

Sun, 01 Jun 2025 00:00:00 +0000

The FEPlab (Free Energy Principle Laboratory) is a collaboration between Eindhoven University of Technology (TU/e) and GN Hearing. The mission of the lab is to ameliorate the participation of hearing-impaired people in formal and informal social settings. The lab will focus its research on transferring a leading physics/neuroscience-based theory about computation in the brain, the Free Energy Principle (FEP), to practical use in human-centered agents such as hearing devices and VR technology.

GN Hearing, which is a globally leading hearing aid manufacturer with a strong research team (of about 20 persons) in Eindhoven, and the TU/e have already been collaborating for many years in BIASlab, which is a research team at the Electrical Engineering department at TU/e. This collaboration has produced theoretical foundations for synthetic FEP-based AI agents. FEPlab has been set up in 2022 and is expected to run until mid-2027. During this time, the partners will continue to develop these FEP agents into a technology that is ready for deployment in the professional hearing device industry.

FEPlab focuses on two Sustainable Development Goals: Goal 3, Good Health and Well-being, and Goal 5, Promote sustained, inclusive, and sustainable economic growth, full and productive employment, and decent work for all. Untreated hearing loss in the elderly increases the risk of developing dementia and Alzheimer’s disease (Ralli et al., 2019) as well as emotional and physical problems (Ciorba et al., 2012). Therefore, this research neatly ties into SDG3 Target 1: reducing premature mortality from non-communicable diseases. Moreover, hearing loss negatively impacts work participation (Svinndal et al., 2018). Hence, this research also ties into SDG8 Target 1: achieve higher levels of economic productivity through technology upgrading and innovation.

The lab comprises experts from different fields of expertise such as Audiology, Autonomous Agents & Robotics, Decision Making, and Machine Learning to tackle the complex multidisciplinary challenges at hand. Socially aware AI and explainable AI are especially important in the lab’s research since the technology needs to be aware of the social context in which it is operating and be able to provide justification for its decisions and actions in a manner that is understandable by humans to ensure its safe use.

Ralli, Massimo, et al. “Hearing loss and Alzheimer’s disease: A Review.” The international tinnitus journal 23.2 (2019): 79-85.
Ciorba, Andrea, et al. “The impact of hearing loss on the quality of life of elderly adults.” Clinical interventions in aging 7 (2012): 159.
Svinndal, Elisabeth Vigrestad, et al. “Hearing loss and work participation: a cross-sectional study in Norway.” International journal of audiology 57.9 (2018): 646-656.

The lab is part of the Innovation Center for Artificial Intelligence

Auto-AR

Fri, 01 Oct 2021 00:00:00 +0000

Automated Situated Design of Augmented Hearing Reality Algorithms

ZERO-AAS

Thu, 01 Jun 2017 00:00:00 +0000

An NWO-TTW Perspectief program called ZERO, organized around 5 projects:

Parking sensors, such that cars can find a free parking slot autonomously.
Monitoring of traffic patterns using advanced audio beam processing.
Autonomous roadside monitoring with video.
Ultra-low power transponders for vulnerable traffic users.
Dependable autonomous computing platforms supporting mobile traffic users.

We were involved in P2, dubbed Autonomous Acoustic Systems (AAS). AAS can be found in various shapes and sizes, ranging from city wide acoustic monitoring systems to hearing aids worn by an individual. To deliver an improved quality and user experience, future generations of these systems should use adaptive signal processing algorithms, while staying within a stringent energy budget for autonomous operation. The AAS project uses two of these systems as driver cases to develop a novel programming paradigm and accompanying ultra-low power implementation platform for a wide range of autonomous acoustic systems.

Industrial partners: Resound, Sorama and Altran

CoHear

Wed, 01 Feb 2017 00:00:00 +0000

Hearing loss is a very serious health condition that has been associated with early dementia and cognitive decline. Still, hearing aid market penetration is quite low, in particular for the large population that is afflicted with ‘mild-to-moderate’ hearing impairment. This is mainly due to two reasons: Stigma (association with old age) and hearing aids (HA) sound quality. The recent commercial introduction of fashionable ‘hearables’ will likely alleviate the stigma issue. Recent advances in machine learning and cloud computing open new avenues for attacking the sound quality issue for hearing aids. In this project, we intend to develop a (crowd-based) collaborative design approach to improving the sound quality issues for the mild-to-moderately hearing-impaired population.

We expect to build a working prototype for collaboratively designed hearing algorithms that can be applied to a new class of ‘smart hearing devices’ with high appeal to the mild-to-moderately hearing-impaired patient. As an additional benefit, we hope that our technology will ease the transition from hearables to professional hearing aid technology for the moderate-to-profound hearing-impaired population.