Do you know a useful tool? Please send your suggestion to resources@computationalaudiology.com

A central hub to share resources related to computational audiology

Here we provide options to share resources that are published on general platforms including OSF, Zenodo, or GitHub among others. Our suggestion to further develop this is by embracing the concept of the distributed data mesh. Also, we collected dedicated auditory toolboxes such as the Auditory Modeling Toolbox (AMT), and the Psychoacoustic Software Package. Remote testing examples include the Remote Testing Wiki, the Portable Automated Rapid Testing (PART), Ecological Momentary Assessment (EMA) and the NIOSH soundlevel meter. The basic idea is to use computationalaudiology.com as a central hub to share resources that are useful for researchers and clinicians.

Purpose:

  • sharing of research software, tools, and models
  • sharing best practices (data policies, software licensing), inspiring peers, and increasing 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

Examples of domain-specific tools and models:

  • Clinical audiology tools, (e.g. interfaces for patients to log their own data)
  • Tools for research scientists who are coding up experiments (e.g. Psychophysics toolbox)
  • Computational models of perception/nervous system (e.g. Auditory Modeling Toolbox)
  • Datasets for research (e.g. Zenodo)

The way to go forward might be the data mesh approach explained below.

Data Mesh

A burgeoning concept aimed at improving the way data can be leveraged beyond its original narrowly scoped intended purpose is the distributed data mesh.

The principal novelty of such an approach is this:

Treat the data you produce like it’s a flagship product.

When the implications of this idea on day-to-day work in research are thoroughly considered, we can see how this drastically differs from the normal process of scientific publication. Consider what would be expected if data acquisition or processing were outsourced to a company. How should we expect them to serve the results back to us? What characteristics should it have? We should certainly be able to trust it. To fulfill this, we should know exactly how it was made, what it is, and how to use it. Accordingly we would expect metadata including exhaustive provenance details and annotations such that the data would be self-evident, i.e. we don’t need help understanding what we’re looking at. It should also be resilient and easy to access (not easy to lose in an accidentally deleted email attachment with no backups). What else would we expect?

The idea of a data mesh was proposed to counter the urge to consolidate data and resources into centrally managed data warehouses or “data lakes.” This was due to the realization that a centralized team could not possible know your data better than youbut to be beneficial, the new approach will require the careful implementation of federation. That is, a balance needs to be maintained between the intellectual/operational autonomy of those producing the data products and community-level consensus on global standards. People and computers need to be able to easily find, access, and understand what’s out there without things becoming too brittle when new ideas come up.

Only recently have journals begun to require data and code as supplements to publication. This is not universal, and even when in place, compliance and enforcement is variable. As encouraging as the progress is when raw datasets and implementations of computational methods are shared with publications, there is vast potential for improvement. For now, it might be enough to begin asking questions like,

  • “If I had access to the data these researchers used in their publication, what could I do with it that they hadn’t thought of?”
  • “How could they have prepared and served up that data to be easier for me to get my hands on?”
  • “Where are the loose ends in the community that standards might help to fix that would help me integrate that data with what I already have?”

Then we can use answers to these to consider and talk about how we could change our own ways of doing things. Thoughtfully identifying and outlining the technical, managerial, institutional and other problems preventing the implementation of this approach is an important prerequisite to finding solutions.

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

 

Zenodo

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


13 documents
  • Ibelings, Saskia, Brand, Thomas, Holube, Inga. (May, 2022). Synthetic Göttingen Sentence Test material created with a text-to-speech system (Version 1). Zenodo. https://doi.org/10.5281/zenodo.6513570
  • tobiasherzke, Paul Maanen, frasherloshaj, hendrikkayser, Marc Joliet, Giso Grimm, steffendasenbrock, Zain Sohail. (February, 2022). HoerTech-gGmbH/openMHA: Release 4.17.0 (Version v4.17.0). Zenodo. https://doi.org/10.5281/zenodo.6281149
  • Kayser, Hendrik, Herzke, Tobias, Maanen, Paul, Zimmermann, Max, Grimm, Giso, Hohmann, Volker. (December, 2021). Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA). Zenodo. https://doi.org/10.1016/j.softx.2021.100953
  • Thalmeier, Dominik, Miller, Gregor, Schneltzer, Elida, Hurt, Anja, Hrabe de Angelis, Martin, Becker, Lore, Müller, Christian L., Maier, Holger. (December, 2021). ABR raw data and results from automated hearing threshold detection. Zenodo. https://doi.org/10.5281/zenodo.5779876
  • Hendrik Kayser, Tobias Herzke, Paul Maanen, Max Zimmermann, Giso Grimm, Volker Hohmann. (December, 2021). Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA). Zenodo. https://doi.org/10.5281/zenodo.5770153
  • Volker Hohmann. (November, 2021). The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation. Zenodo. https://doi.org/10.5281/zenodo.5727778
  • Volker Hohmann. (November, 2021). Raw data and scripts for "The Period-Modulated Harmonic Locked Loop (PM-HLL): A low-effort algorithm for rapid time-domain multi-periodicity estimation". Zenodo. https://doi.org/10.5281/zenodo.5727729
  • Sanchez-Lopez, Raul, Nielsen, Silje Grini, El-Haj-Ali, Mouhamad, Bianchi, Federica, Fereczkowski, Michal, Cañete, Oscar, Wu, Mengfan, Neher, Tobias, Dau, Torsten, Sébastien Santurette. (June, 2021). Data from "Auditory tests for characterizing hearing deficits in listeners with various hearing abilities: The BEAR test battery" (Version v1.1). Zenodo. https://doi.org/10.5281/zenodo.4923009
  • 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
  • Nuesse, Theresa, Wiercinski, Bianca, Holube, Inga. (February, 2021). Synthetic German matrix speech test material created with a text-to-speech system (Version Version 1 (zipped)). Zenodo. https://doi.org/10.5281/zenodo.4522088

 

GitHub

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. You can make your existing repository better findable by adding a topic to your repositories which acts as a ‘tag/label’. We recommend adding the topic ‘Computational Audiology’, and maybe additional label including ‘Cochlear Model’ / [specific topic] / etc to GitHub repositories you wish to share with the computational audiology community.

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

AIDA

AIDA is an Active Inference-based Design Agent that aims at real-time situated client-driven design of audio processing algorithms. Here is a link to the accompanying paper (Podusenko et al., 2022).


https://github.com/biaslab/AIDA
0 forks.
3 stars.
0 open issues.

Recent commits:

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.


https://github.com/elleobrien/Lab_Computing_Resources
1 forks.
7 stars.
0 open issues.

Recent commits:

Spectral and temporal modulation detection

Adam Bosen implemented 3 alternative forced choice adaptive modulation detection tasks to estimate detection thresholds in jsPsych, which allows these tasks to be conducted through a web browser.

 

Listen-and-repeat

Phoneme Alignment

Tilak J. Ratnanather, Lydia C. Wang, Seung-Ho Bae, Erin R. O’Neill, Elad Sagi and Daniel J. Tward created scripts for analyzing phoneme errors from speech perception tests. To appear in Frontiers in Neurology: Digital Hearing Healthcare, ‘Visualization of Speech Perception Analysis via Phoneme Alignment: a pilot study.’

WHISPER

Here is the Python code from the WHISPER (Widespread Hearing Impairment Screening and PrEvention of Risk) project. You can use it for training and evaluation of machine learning models for hearing loss detection through speech-in-noise testing and post-hoc explainability analysis (SHapley Additive exPlanations-SHAP, Partial Dependence Plots-PDPs, and Feature Permutation Importance) applied to non-natively explainable models (e.g., Random Forests). The code is made available by Alessia Paglialonga and Marta Lenatti.

 

3D Audio Spatialiser and Hearing Aid and Hearing Loss Simulation

The 3DTI Toolkit is a standard C++ library for audio spatialisation and simulation using headphones developed within the 3D Tune-In (3DTI) project (http://www.3d-tune-in.eu).The Toolkit allows the design and rendering of highly realistic and immersive 3D audio, and the simulation of virtual hearing aid devices and of different typologies of hearing loss.

Technical details about the 3D Tune-In Toolkit spatialiser are described in:

Cuevas-Rodríguez M, Picinali L, González-Toledo D, Garre C, de la Rubia-Cuestas E, Molina-Tanco L and Reyes-Lecuona A. (2019) 3D Tune-In Toolkit: An open-source library for real-time binaural spatialisation. PLOS ONE 14(3): e0211899. https://doi.org/10.1371/journal.pone.0211899

human cochlear filter models

There have been developed several advanced (non-linear) models to simulate characteristics of the human auditory system. Saremi et al. (2016) compared 7 contemporary models, see below figure. By sharing the computer code we hope to improve the understanding of the many intricacies of the models and to facilitate direct comparisons or to help you to select the appropriate model for your aim. A number of these models are implemented in the Auditory Modeling Toolbox.

The selected models by Saremi et al (2016) and the modeling approaches they belong to. The models are named after their first authors’ name unless they have been given a specific technical name.

Software for “Frequency analysis and synthesis using a Gammatone filterbank”

In the Zenodo community, you can find the Matlab implementation of the gammatone filterbank described in Volker Hohmann’s Paper `Frequency analysis and synthesis using a Gammatone filterbank’ (Hohmann, 2002).  It uses numerical methods described in `Improved numerical methods for gammatone filterbank analysis and synthesis’ by T. Herzke and V. Hohmann (Herzke & Hohmann, 2007)

CARFAC

The CAR-FAC (cascade of asymmetric resonators with fast-acting compression) is a cochlear model implemented as an efficient sound processor, for mono, stereo, or multi-channel sound inputs. It has been created by Richard F. Lyon. The model is introduced in Richard’s book: ‘Human and Machine Hearing: Extracting Meaning from Sound‘ and in Lyon, 2011.  This package (upper GitHub repository) includes Matlab and C++ implementations of the CARFAC model as well as code for computing Stabilized Auditory Images (SAIs). A jupiter Notebook version was written by Andre van Schaik. See the design doc for a more detailed discussion of the software design.

A CARFAC object knows how to design its details from a modest set of parameters, and knows how to process sound signals to produce “neural activity patterns” (NAPs). It includes various sub-objects representing the parameters, designs, and states of different parts of the CAR-FAC model. The CARFAC class includes a vector of “ear” objects — one for mono, two for stereo, or more. The three main subsystems of the EAR are the cascade of asymmetric resonators (CAR), the inner hair cell (IHC), and the automatic gain control (AGC). These are not intended to work independently, but each part has three groups of data associated with it. A recent description of CARFAC and how to address quadratic distortions is provided in Saremi & Lyon (2018).

Further reading: Lyon, R. F. (2017). Human and Machine Hearing. Cambridge University Press.

A Jupyter Notebook version (GitHub Repository below) was written by Andre van Schaik based on Dick Lyons’ book Human and Machine Hearing (this links to his blog).

Physiological models for auditory-nerve and midbrain neurons

The University of Rochester (UR) developed a GUI interface to provide visualizations of population responses of auditory-nerve (AN) and inferior colliculus (IC) model neurons. The underlying models have been included in the AMT (see below) and modelDB (see below). A cloud-based version of the UR_EAR GUI is now available as a web app (https://urhear.urmc.rochester.edu)  Open-source MATLAB code is available on the Open Science Framework (OSF) site: https://osf.io/6bsnt/.

ModelDB

ModelDB provides a well-curated accessible and searchable database of computational models of neurons including files for running the models. You can browse or search through over 1740 models. A ModelDB entry contains a model’s source code, description, metadata, and a citation of the accompanying peer-reviewed publication. The model’s source code can be in any programming language for any environment. The database was created and is maintained by the SenseLab. For further information, see model sharing in general and ModelDB in particular.

The Auditory Modeling Toolbox (AMT)

The Auditory Modeling Toolbox (AMT) is a  MatlabGNU Octave toolbox intended to serve as a common ground for reproducible research in auditory modeling (Majdak et al., 2021). 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. The most recent version AMT 1.1 for Matlab and Octave contains over 60 models from various areas of auditory research and written in various programming languages. The AMT documentation website can be found at https://amtoolbox.org. Further, over 40 GB of auxiliary data and cached modeling results are available for download on the fly. The AMT 1.1 has been tested for Matlab and Octave on Windows and Linux. Two release packages, the “full” and the “code-only”, are available for download.

In December 2021 the Auditory Modeling Toolbox was updated including new models (mostly dealing with binaural speech intelligibility), updates of other models, as well as many bug fixes and improvements. The detailed changes are enlisted here. The AMT 1.1 is a community-driven project and would not exist without our help! If you find a bug or wish an improvement, please create a ticket in the “Bug and feature request” section of the project.

Currently, the AMT core team consists of: Piotr Majdak, Clara Hollomey, Robert Baumgartner, and Michael Mihocic. Overall there is a very large group of contributors enlisted on the AMT website.

Link to the GitHub (old version?):

Psychophysics Toolbox (PTB-3)

The Psychophysics Toolbox Version 3 (PTB-3) is a free toolbox of Matlab and GNU Octave functions for vision and neuroscience research. The functions are also useful for auditory research. It makes it easy to synthesize and show accurately controlled visual and auditory stimuli and interact with the observer. A subset of its functionality is also available as part of Python based toolkits like PsychoPy.
The Psychophysics toolbox is easy to use also if you have little programming experience. Using the toolbox you learn about Matlab itself, how to create stimuli and measure responses, and how to organize an experiment. The included demos illustrate how many common tasks may be accomplished (see PsychDemos or type >> help PsychDemos in Matlab after installation). Matlab’s help feature is one of Matlab’s better features and is helpful to learn by trial and error.

The Psychtoolbox core development team consists of David Brainard, Mario Kleiner, Denis Pelli and Tobias Wolf. Follow this link to get in touch.

Below you find the main GitHub repository for development of Psychtoolbox-3. It is meant for developers or alpha-testers only, not for regular users! Regular users please download the toolbox here.

Psychoacoustic Software Package (by Moore & Sek)

Professor Aleksander Sęk and Professor Brian Moore developed a software package that allows a wide variety of experiments in psychoacoustics without the need for time-consuming programming or technical expertise. The only requirements are a personal computer (PC not Apple) with a good-quality sound card (preferably an external sound card) and a set of headphones.  The software is intended for students of psychoacoustics and related disciplines, such as audiology and audio engineering, who want to try psychoacoustic experiments for themselves. The software should also be useful for researchers who want to run experiments without the need to spend time writing computer software to generate the stimuli, run the experiment, and gather the data. The software can be downloaded here: with English / Polish instructions and we recommend buying the accompanying book “Guide to PSYCHOACOUSTICS” by Sęk, A. P., and Moore, B. C. J. (2021).  (Adam Mickiewicz University Press, Poznan, Poland), pp. 348. DOI: 10.14746/amup.9788323239321. You can buy the book via the publisher’s website.

Remote testing wiki

The Task Force on Remote Testing, an initiative of the Technical Committee on Psychological and Physiological Acoustics (PP) of the Acoustical Society of America (ASA), created a wiki with information and examples for remote testing of information about approaches to data collection outside the lab, for example in participants’ own homes. They also provide advantages of remote testing, such as large-N studies and access to special populations. See https://www.spatialhearing.org/remotetesting/Main/HomePage

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.

Other web-based methods

Adam Bosen developed web-based methods for testing speech recognition, psychophysics, and working memory. See his personal website and some Github repositories above.

 

Zotero

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.

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

olMEGA – an open-source toolkit for Ecological Momentary Assessment

Ecological Momentary Assessment (EMA) is a method for collecting data momentarily and repeatedly in natural environments with electronic devices (Holube et al., 2020). olMEGA is a toolkit for EMA developed at the Institute of Hearing Technology and Audiology of Jade University of Applied Sciences, Oldenburg, Germany. The olMEGA system is described in Kowalk et al. (2020) and was used to evaluate hearing aid benefit in real life (von Gablenz et al., 2021). The open-source toolkit offers software for the creation of questionnaires running on Android smartphones as well as software for analysis and data-handling. All tools are available at https://github.com/ol-MEGA. The latest builds of some olMEGA-Tools can be found here: https://tgm.jade-hs.de/media/olMEGA/.

The following list describes the main tools

olMEGA_MobileSoftware_V2 The source code for the Android APK file (Java)
olMEGA_Handbook Instruction manual
olMEGA_DataService_ServerSQL Databank server software (Django based, Python)
olMEGA_DataService_ClientThe client for the SQL server (Python)
olMEGA_DataExtractionTool to import the data from mobile devices to the computer (Matlab, adb)

Tools for Own Voice Detection (Matlab)

Tools for data analysis (Matlab)

Cognition and Natural Sensory Processing Workshop (CNSP) resources

For researchers interested in studying natural speech or music perception with EEG/MEG/ECoG, the organizers of the CNSP collected a number of resources including tutorials and lectures. For data sharing and standardization purposes, the group adopted a Continuous-event Neural Data data format. Please check out their CND data format (Continuous-event Neural Data) here. Detailed insights on the format can be found in the data-preparation guidelines.

e-Audiology tools for clinicians

Below you find several online tools that could be used in clinics to administer hearing test, screen for hearing loss or other to determine safe listening levels.

 

DIN-test

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 (De Sousa et al, 2020).In addition, the test can distinguish conductive hearing loss from sensorineural hearing loss, while keeping test duration to a minimum by testing binaurally.

Here is a short 2-minute video from HearX that shared the online DIN-test.

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

 

 

BASE

The Basic Auditory Skills Evaluation (BASE) battery comprises 17 brief online tests. It was designed by Shafiro et al. to provide a comprehensive assessment of patient performance at-home or in-clinic. Please have a look at the presentation at the Internet&Audiology 2021 conference for further background.

PART

Portable Automated Rapid Testing (PART) includes a wide variety of auditory tasks, all of which have been shown to have utility in assessing auditory function in the laboratory. The set of tasks were chosen by the PART development team, which is led by Frederick J. Gallun at the Oregon Health & Science University, David Eddins at the University of South Florida, and Aaron Seitz at the University of California Riverside (UCR). The program is a production of the UCR Brain Game Center, a research unit focused on brain fitness methods and applications.

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.

Automated Speech Recognition apps

Several apps have been developed for people with hearing loss to transcribe speech to text. Here are links to download the apps on your smartphone or tablet: AVA (iOS, Android), Earfy (iOS, Android), Live Transcribe (Android), Speechy (iOS), and NALscribe (iOS).  NALscribe was specifically developed for use in Audiology Centers. Have a look at the 2-minute video about the design and development of the app. Pragt et al. (2021) evaluated the audiological performance of 4 of the above apps. Loizidis et al. (2020) describe novel use cases for such apps, including communication with people wearing a facemask or through closed glass surfaces doors.

Acknowledgments

We would like to thank Giovanni  Di Liberto, Laurel H. Carney, Inga Holube, Richard F. Lyon, Alessia Paglialonga, Marta Lenatti, Piotr Majdak, Clara Hollomey, Josh McDermott, Elle O’Brien, Adam Bosen, Tilak J. Ratnanather, Frederick J. Gallun, Valeriy Shafiro Brian C.J. Moore, Volker Hohmann, and Raul Sanchez-Lopez for making software and data freely available.

References

Cuevas-Rodríguez M, Picinali L, González-Toledo D, Garre C, de la Rubia-Cuestas E, Molina-Tanco L and Reyes-Lecuona A. (2019) 3D Tune-In Toolkit: An open-source library for real-time binaural spatialisation. PLOS ONE 14(3): e0211899. https://doi.org/10.1371/journal.pone.0211899

Herzke, T., & Hohmann, V. (2007). Improved numerical methods for gammatone filterbank analysis and synthesis. Acta Acustica United with Acustica, 93(3), 498–500.

Hohmann, V. (2002). Frequency analysis and synthesis using a Gammatone filterbank. Acta Acustica United with Acustica, 88(3), 433–442.

Holube, I., von Gablenz, P., & Bitzer, J. (2020). Ecological Momentary Assessment in Hearing Research: Current State, Challenges, and Future Directions. Ear and Hearing, 41, 79S. https://doi.org/10.1097/AUD.0000000000000934

Kowalk, U., Franz, S., Groenewold, H., Holube, I., von Gablenz, P., & Bitzer, J. (2020). olMEGA: An open source android solution for ecological momentary assessment. GMS Zeitschrift Für Audiologie – Audiological Acoustics, 2, Doc08. https://doi.org/10.3205/zaud000012

Lyon, R. F. (2011). Cascades of two-pole–two-zero asymmetric resonators are good models of peripheral auditory function. The Journal of the Acoustical Society of America, 130(6), 3893–3904.

Lyon, R. F. (2017). Human and Machine Hearing. Cambridge University Press.

Majdak, P., Hollomey, C., & Baumgartner, R. (2021). AMT 1.0: The toolbox for reproducible research in auditory modeling. Submitted to Acta Acustica.

Podusenko, A., van Erp, B., Koudahl, M., & de Vries, B. (2022). AIDA: An Active Inference-based Design Agent for Audio Processing Algorithms. Frontiers in Signal Processing, 2, 842477. https://doi.org/10.3389/frsip.2022.842477

Ratnanather, J., Wang, L., Bae, S.-H., O’Neill, E., Sagi, E., & Tward, D. (n.d.). Visualization of Speech Perception Analysis via Phoneme Alignment: A Pilot Study. Front. Neurol., 12:724800. https://doi.org/10.3389/fneur.2021.724800

Saremi, A., Beutelmann, R., Dietz, M., Ashida, G., Kretzberg, J., & Verhulst, S. (2016). A comparative study of seven human cochlear filter models. The Journal of the Acoustical Society of America, 140(3), 1618–1634.

Saremi, A., & Lyon, R. F. (2018). Quadratic distortion in a nonlinear cascade model of the human cochlea. The Journal of the Acoustical Society of America, 143(5), EL418–EL424.

De Sousa, K. C., Swanepoel, D. W., Moore, D. R., Myburgh, H. C., & Smits, C. (2020). Improving Sensitivity of the Digits-In-Noise Test Using Antiphasic Stimuli. Ear and Hearing41(2), 442–450. https://doi.org/10.1097/AUD.000000000000077

von Gablenz, P., Kowalk, U., Bitzer, J., Meis, M., & Holube, I. (2021). Individual Hearing Aid Benefit in Real Life Evaluated Using Ecological Momentary Assessment. Trends in Hearing, 25, 2331216521990288. https://doi.org/10.1177/2331216521990288

 

Updates

January 6, 2022 by Jan-Willem Wasmann. Updates: AMT 1.1, WHISPER, CARFAC, PTB-3.

January 14, 2022 by Jan-Willem Wasmann. Updates: Olmega

February 14, Whisper Github link fixed.

July 2, UR_EAR and CNSP resources added

July 4, 3D tune-in model and ModelDB added