-Experimental page- Most resources are tailored to researchers, tools for clinicians will be added at a later stage.


A central hub to share resources related to computational audiology

Here we will draft options to share resources that are published on general platforms including OSF, Zenodo or GitHub among others. Also we collected dedicated auditory toolboxes such as the Auditory Modeling Toolbox (AMT). The basic idea is to use computationalaudiology.com as a central hub to share resources that are useful for researchers and clinicians.


  • sharing of research software, tools and models
  • sharing best practices (data policies, software licensing), inspire peers, and increase transparency
  • facilitating cooperation across centers increase sample sizes and strengthen the robustness of experimental evaluations
  • building a community that fosters effective collaboration and uses similar tools and data sharing pipelines

For Researchers

Open Science

Here is the definition of open science found on Wikipedia: “the movement to make scientific research (including publications, data, physical samples, and software) and its dissemination accessible to all levels of an inquiring society, amateur or professional. Open science is transparent and accessible knowledge that is shared and developed through collaborative networks. It encompasses practices such as publishing open research, campaigning for open access, encouraging scientists to practice open-notebook science, and generally making it easier to publish and communicate scientific knowledge[1][2]. The are multiple large initiatives to stimulate open science. For instance, the non for profit Open Science Foundation (OSF) is creating all kinds of tools to disseminate knowledge. The European project, Zenodo is a large open repository branched from CERN.

Open Science Foundation (OSF)

[add here API to sync collections]

Below a short clip that highlights some of the tools created by OSF



We created a Zenodo community for computational audiology. The aim of this community is to share data, code and tools useful for computational audiology and related fields such as digital hearing health care and AI in health care. Resources will be integrated on the computationalaudiology.com forum in order to make the data, code and tools easier to find for researchers and clinicians. Researchers that have shared a repository or project on Zenodo can edit their repository to request to add their work to the community. To add an existing repository you need to edit the existing entry, and then you see the screen below (example).

Screenshot from Zenodo when adding a community by editing a repository


Look for the community field and search the community you want to add, here “computational audiology. When found, press enter to add the community. Then press save at the top of the form. After pressing “save” you have to press “publish” for the saved changes to take effect. The curator will receive a message regarding the inclusion of the entry in the community. For a new entry the form looks the same, including the community section. A new repository can be added via this link.

Zenodo Repositories from the Computational Audiology community

7 documents
  • tobiasherzke, Paul Maanen, frasherloshaj, hendrikkayser, Marc Joliet, genckamil, steffendasenbrock, Giso Grimm, Muhammad Zain Sohail. (March, 2021). HoerTech-gGmbH/openMHA: Release 4.15.0 (Version v4.15.0). Zenodo. https://doi.org/10.5281/zenodo.4650058
  • Zinner, Christina, Winkler, Alexandra, Holube, Inga. (March, 2021). Speech Adjusted Noises (SAN) for German speech recognition tests. Zenodo. https://doi.org/10.5281/zenodo.4609783
  • Hendrik Kayser, Tobias Herzke, Paul Maanen, Max Zimmermann, Giso Grimm, Volker Hohmann. (March, 2021). Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA). Zenodo. https://doi.org/10.5281/zenodo.4601604
  • Raul Sanchez-Lopez, Fereczkowski, Michal, Santurette, Sébastien, Dau, Torsten, Neher, Tobias. (April, 2020). Data and materials from: "Towards Auditory Profile-based Hearing-aid Fitting: Fitting Rationale and Pilot Evaluation" (Version 1.1). Zenodo. https://doi.org/10.5281/zenodo.4421553
  • Sanchez-Lopez, Raul, Nielsen, Silje Grini, El-Haj-Ali, Mouhamad, Bianchi, Federica, Fereczkowski, Michal, Cañete, Oscar, Wu, Mengfan, Neher, Tobias, Dau, Torsten, Sébastien Santurette. (December, 2019). Data from "Auditory tests for characterizing hearing deficits: The BEAR test battery" (Version v1.0). Zenodo. https://doi.org/10.5281/zenodo.3459580
  • Hohmann, Volker, Herzke, Tobias. (February, 2007). Software for "Frequency analysis and synthesis using a Gammatone filterbank" (Version 1.1). Zenodo. https://doi.org/10.5281/zenodo.2643400
  • Josupeit, Angela, Schoenmaker, Esther, van de Par, Steven, Hohmann, Volker. (May, 2018). Raw data for "Sparse periodicity-based auditory features explain human performance in a spatial multi-talker auditory scene analysis task". Zenodo. https://doi.org/10.5281/zenodo.1246836



GitHub is the largest platform for code hosting that enables version control and collaboration. It lets you and others work together on projects from anywhere. Here we will collect useful code and repositories for auditory experiments, modeling, data processing and analyses.

Clarity project

The Clarity project is running a series of machine learning challenges to revolutionize signal processing in hearing aids. The first enhancement challenge has just been launched. For more information go to claritychallenge.org. Below you find the GitHub repository

Headphone check

At the McDermott lab a headphone screening task was developed to facilitate web-based experiments employing auditory stimuli. The efficacy of this screening task has been demonstrated in . The headphone check is intended to precede the main task(s), and should be placed at or near the beginning of an online experiment. Participants who pass are allowed through to the remainder of the experiment, but those who do not pass should instead be routed to an ending page and must leave the experiment after screening.

Computing principles for scientific researchers

An overview of lesson’s learned about data management, software development, and operations collected by Elle O’Brien.

The Auditory Modeling Toolbox (AMT)

The Auditory Modeling Toolbox (AMT) is a Matlab/Octave toolbox intended to serve as a common ground for reproducible research in auditory modeling. On the accompanying website, AMT states that it provides models for many stages of the auditory system, ranging from HRTFs modeling the outer- and middle-ear acoustics, various cochlear filters, inner-hair cell models, binaural processing, up to speech intelligibility. The models are backed-up by publications. In addition to the models, data from experiments are included allowing to reproduce results from publications.


This is an example of a Zotpress in-text citation . Place a bibliography shortcode somewhere below the citations. This will generate the in-text citations and a bibliography.


Wasmann, J.-W. A., & Barbour, D. L. (2021). Emerging Hearing Assessment Technologies for Patient Care. The Hearing Journal, 74(3), 44. https://doi.org/10.1097/01.HJ.0000737596.12888.22
Barbour, D. L., & Wasmann, J.-W. A. (2021). Performance and Potential of Machine Learning Audiometry. The Hearing Journal, 74(3), 40. https://doi.org/10.1097/01.HJ.0000737592.24476.88
Wasmann, J.-W. A., Lanting, C. P., Huinck, W. J., Mylanus, E. A. M., van der Laak, J. W. M., Govaerts, P. J., Swanepoel, D. W., Moore, D. R., & Barbour, D. L. (2021). Computational Audiology: New Approaches to Advance Hearing Health Care in the Digital Age. Ear and Hearing, Publish Ahead of Print. https://doi.org/10.1097/AUD.0000000000001041
Wilson, B. S., Tucci, D. L., Merson, M. H., & O’Donoghue, G. M. (2017). Global hearing health care: new findings and perspectives. The Lancet, 390(10111), 2503–2515. https://doi.org/10.1016/S0140-6736(17)31073-5

e-Audiology tools for clinicians

For the 13th ARO symposium in February 2021 we created a demonstration page to administer online a Digits-In-Noise Test Using Antiphasic Stimuli. The researchers found that the antiphasic digit presentation improved the sensitivity of the DIN test to detect sensorineural hearing loss . In addition, the test can distinguish conductive hearing loss from sensorineural hearing loss, while keeping test duration to a minimum by testing binaurally.

The Hörtech expert center developed also a web-based implementation of the DIN-test. [add sources]

Last year Chris Stecker, chair of the ASA P&P Task Force on Remote Testing, performed a survey to collect experiences and resources for remote testing. A wiki-based webpage is created that contains discussions, best practices, and links to other resources related to remote testing.

NIOSH Sound Level Meter App

The National Institute for Occupational Safety and Health (NIOSH) has developed a Sound Level Meter (SLM) app that combines the best features of professional sound levels meters and noise dosimeters into a simple, easy-to-use package. The app was developed to help workers make informed decisions about their noise environment and promote better hearing health and prevention efforts. Download the app for iOS. More information from NIOSH.

Close-up of downloaded Sound app.