Submitted by:

Danielle Benesch, Centre for interdisciplinary Research in Music, Media and Technology, McGill University
Nithin Raj, Rachel Bouserhal and Jérémie Voix, NSERC-EERS Industrial Research Chair in In-ear Technologies, École de technologie supérieure, Montréal, Canada.

Real-time audio processing requires substantial memory and processing power. The Tympan Rev-E open source hearing-aid hardware, fitted with a fast Teensy 4.1 processor, is capable of handling most audio processing needs. However, some audio applications utilize resource-heavy algorithms which require higher memory and processing power. As part of the ASA 2021 Design Challenge, the research team submitted a study with such an audio application. The team designed a system comprising a Tympan hardware, an external personal computer (PC) and a software pipeline layer to interface between the two. When the team started working on this project, there was no way to communicate this type of extensive audio data between Tympan and a PC. Now, anyone can do it!

The application for this system was tried in a case study: a smart in-ear hearing protection device for people with decreased sound tolerance (DST), designed to alleviate sound-induced distress. Conventional hearing protectors attenuate all sounds, posing some significant drawbacks. Firstly, since the distressing sounds and relevant sounds, such as speech, are attenuated equally, relevant sounds can get lost and the engagement with the surroundings is reduced. Secondly, frequently wearing hearing protection to block out everyday sounds can cause the wearer to become accustomed to the quiet, which in turn may worsen tolerance to sound over time. To mitigate these effects a system was proposed to manage DST by attenuating only when necessary. This has the advantage of alleviating sound-induced distress while being able to understand one’s interlocutor.

To achieve this the electroacoustic earpieces need to be connected to a system that runs a number of algorithms to perform a number of tasks. Tympan is the best platform for tasks that require low-latency, while the tasks for which latency is less of a requirement but computing power is, are best performed on a PC. Using both a mini-PC and the Tympan with a software audio pipeline to interface between the two turned out to be the suitable solution for this complex case study. The Tympan could be used for processing surrounding sounds, for example with dynamic range limiting and masking noise generation, while the PC could be used to adapt the audio processing, for example, with the detection of voices and distressing sounds.

The Tympan hardware acquires the input audio from the Tympan’s onboard microphone and/or the external microphones. The Tympan is recognized by the PC as a soundcard interface, and the input data is transmitted to the PC via USB/Serial communication to be processed by the algorithm running on the PC. The output of the PC algorithm is then sent back to the Tympan, through USB/serial communication, so that the underlying audio processing parameters are adjusted.

This combination of existing technologies is a promising open-source hardware & software solution for decreased sound tolerance research. During their presentation at the ASA 2021 conference in Seattle the researchers hinted on potential improvements to this system such as using wireless transmission between the Tympan and the PC, developing a graphical user interface to inform device usage, and including software to record “in situ” research data.

For more information information on the research by NSERC-EERS please visit:



About the TYMPAN ASA2021 Design Challenge

During the ASA (Acoustical Society of America) conference in June 2021, Tympan hosted a design challenge: What is possible with the Tympan?

10 exciting new applications were submitted and presented at the following ASA conference: Enhancements of hearing aids, spatial acoustic processing and smart earphones and much more. Stay tuned if you want to learn what is possible and to keep track of future developments.