[AUDITORY] Computational Audiology - New Year's Update

["Wasmann, Jan-Willem" <Jan-Willem.Wasmann@xxxxxxxxxxxxx>]

Dear list (and potential journal club aficionados),

Below is an update from the Computational Audiology Network. Launching a community-driven Computational Audiology journal is probably a bridge too far at this point. While a journal may not be feasible, we propose an alternative method of testing the waters by initiating a journal club within our network via The Peer Recommender Challenge! The idea is to recommend literature to your peers. We kindly invite your thoughts and feedback on this idea, see below (or this link: https://computationalaudiology.com/quarterly-update-q6-q8/#peer). Your participation in this initiative will be greatly appreciated.

Quarterly update Q6-Q8 - Computational Audiology In this edition, we will be discussing real meetings: virtual versus in-person, our online presence through the Computational Audiology Network (CAN) on LinkedIn, upcoming events in 2023, a report on the VCCA 2022 conference, tips for researchers, job opportunities, and an overview of 2022 (Q6-Q8) quarter’s publications related to computational audiology. computationalaudiology.com

Best regards,

Jan-Willem Wasmann 

Computational Audiology Network Team

Peer Recommender Challenge!

Would you benefit from an online journal club? We are excited to announce the Peer Recommender Challenge, where we invite you to share and recommend papers published between April and December 2022 relevant to our audience. A list of the papers we have compiled thus far can be found below and on the computational audiology website. To participate, kindly provide a brief and informative summary of the highlights of the paper in the comments section. Our team will review and approve each comment, and with the assistance of the advanced language model, ChatGPT, we will compile all recommendations into a cohesive narrative accompanied by relevant graphics, similar to our previous efforts in Q5. Join us in this endeavor to foster a collaborative and informed Computational Audiology community.

 

In this edition, the Peer Recommender Challenge! Virtual or in-person meetings? CAN on LinkedIn, research tips, job opportunities and upcoming events

New year's update

In this edition, we will be covering a variety of topics, including the Peer Recommender Challenge, real meetings: virtual versus in-person, the Computational Audiology Network (CAN) online presence on LinkedIn, upcoming events in 2023, a report on the VCCA 2022 conference, tips for researchers, job opportunities, and an overview of 2022 (Q6-Q8) quarter’s publications related to computational audiology. We also would like to thank everybody who contributed this year to the VCCA and CAN.

Wishing you a happy and prosperous New Year from the field of Computational Audiology. May the advancements in technology continue to bring new solutions to hearing challenges.

Peer Recommender Challenge!

Would you benefit from an online journal club? We are excited to announce the Peer Recommender Challenge, where we invite you to share and recommend papers published between April and December 2022 relevant to our audience. A list of the papers we have compiled thus far can be found below and on the computational audiology website. To participate, kindly provide a brief and informative summary of the highlights of the paper in the comments section. Our team will review and approve each comment, and with the assistance of the advanced language model, ChatGPT, we will compile all recommendations into a cohesive narrative accompanied by relevant graphics, similar to our previous efforts in Q5. Join us in this endeavor to foster a collaborative and informed Computational Audiology community.
 

Real Meetings: Virtual Versus In-Person

We have recently published a blog on the topic of virtual and in-person meetings and their impact on the growth and strength of the Computational Audiology Network. Special thanks to Seba Ausili, Liepollo Ntlhakana (PhD), Bill Whitmer, Soner Türüdü, Robert Eikelboom, Elle O'Brien, Deniz Başkent, Dennis Barbour, David Moore and Charlotte Garcia for sharing their experiences and advice on how to navigate these types of meetings.

Computational Audiology Network (CAN presence on LinkedIn)

To further expand our online presence, we have created a public company page for CAN on LinkedIn (keeping a low profile on twitter). This page will feature updates on our activities and is meant to inform and engage all stakeholders, including researchers, audiologists, patients, and (future) funders. Follow us via (https://www.linkedin.com/company/computational-audiology-network/). Please reach out to me if you would like to contribute or reply to this email). If you become a CAN member, you can easily share your research progress and achievements via our company page.

In addition, we also have a closed discussion group on LinkedIn for more in-depth conversations among peers. https://www.linkedin.com/groups/8931734/, here you can freely express your opinion and insights.

 

VCCA2022 Conference Report

The VCCA 2022 conference was held online by Hearing4all (University of Oldenburg and Hannover Medical School) on June 30th and July 1st. The conference featured five keynote talks, four special sessions, and over 500 registered participants, making it another successful year for the conference. Read the full conference report here.

 

 

2022 in numbers

• >500 participants VCCA2022
• >15.000 visitors of computationalaudiology.com
• 5 videos released on Computational Audiology TV
• Total number of publications in 2022
  • 6 posts
  • 81 publications found with google scholar
  • 2 repositories added to Zenodo
  • >10 tools and software packages added to the resources
  • 2 “quarterly” updates:  Q5 and Q6-Q8

 

Tip for researchers

If you are planning on publishing a paper related to computational audiology, consider adding the key word [Computational Audiology] to make it easier for your peers to find. The same goes for code added to GitHub or HuggingFace. In GitHub, you can make your existing repository better findable by adding a topic to your repository 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.

For finalized models and datasets, we have a Zenodo community for computational audiology. The goal of this community is to share data, code, and tools that are useful for our field and related fields such as digital hearing health care.

Job opportunities

Want to join the Deep Hearing Lab at the University of Cambridge? Or work in the Audio Algorithms team at Apple? Check out these and other job openings in computational audiology in academia and industry.

 

Acknowledgments

We would like to thank everybody who contributed to the website, shared software or datasets, or presented at the VCCA2022. A special thanks to: Giovanni  Di Liberto, Laurel H. Carney, Inga Holube, Richard F. Lyon, Alessia Paglialonga, Marta Lenatti, Piotr Majdak, Clara Hollomey, and Raul Sanchez-Lopez for making software and data freely available on our resources page.

Upcoming Events 

ARO 2023

ICRA 2023

ESPCI 2023

ICASSP 2023

VCCA2023

View all upcoming events

Recent publications related to computational audiology

Are there any papers you would like to recommend? Please join our peer recommender challenge! Below you can find a we have collected so far. Please write a short description with the highlights why you recommend the paper in the comments on the CAN web page. We will approve the comment and use ChatGPT to compile all comments into a running story including graphics as we did before for the papers published in Q5.   

It is nice to see so many VCCA talks turn into papers and pre-prints. Below publications were found using  [(Computational Audiology) OR ((Machine Learning) AND audiology)] in Google Scholar.  Did we miss a publication? Please send your suggestion to resources@xxxxxxxxxxxxxxxxxxxxxxxxxx. If you are going to publish a paper related to computational audiology, consider adding as key word [Computational Audiology’] to make your paper better findable for your peers and us.

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Alonso-Valerdi, L. M. (2022). Analysis of Electrophysiological Activity of the Nervous System: Towards Neural Engineering Applications. In Biometry. CRC Press.

Alonso-Valerdi, L. M., Torres-Torres, A. S., Corona-González, C. E., & Ibarra-Zárate, D. I. (2022). Clustering approach based on psychometrics and auditory event-related potentials to evaluate acoustic therapy effects. Biomedical Signal Processing and Control, 76, 103719. https://doi.org/10.1016/j.bspc.2022.103719

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Chan, J., Glenn, A., Itani, M., Mancl, L. R., Gallagher, E., Bly, R., Patel, S., & Gollakota, S. (2022). Wireless earbuds for low-cost hearing screening (arXiv:2212.05435). arXiv. http://arxiv.org/abs/2212.05435

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Drakopoulos, F., & Verhulst, S. (2022). A Differentiable Optimisation Framework for The Design of Individualised DNN-based Hearing-Aid Strategies. ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 351–355. https://doi.org/10.1109/ICASSP43922.2022.9747683

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Fawcett, T. J., Longenecker, R. J., Brunelle, D. L., Berger, J. I., Wallace, M. N., Galazyuk, A. V., Rosen, M. J., Salvi, R. J., & Walton, J. P. (2023). Universal automated classification of the acoustic startle reflex using machine learning. Hearing Research, 428, 108667. https://doi.org/10.1016/j.heares.2022.108667

Guiraud, P., Moore, A. H., Vos, R. R., Naylor, P. A., & Brookes, M. (2022). Machine Learning for Parameter Estimation in the MBSTOI Binaural Intelligibility Metric. 2022 International Workshop on Acoustic Signal Enhancement (IWAENC), 1–5. https://doi.org/10.1109/IWAENC53105.2022.9914725

Haglund, A. (n.d.). Artificial Intelligence for Sign Language Recognition and Translation. 47.

Harris, K. C., & Bao, J. (2022). Optimizing non-invasive functional markers for cochlear deafferentation based on electrocochleography and auditory brainstem responses. The Journal of the Acoustical Society of America, 151(4), 2802–2808. https://doi.org/10.1121/10.0010317

Investigations on the Deep Learning Based Speech Enhancement Algorithms for Hearing-Impaired Population—ProQuest. (n.d.). Retrieved October 18, 2022.

Jeng, F.-C., & Jeng, Y.-S. (2022). Implementation of Machine Learning on Human Frequency-Following Responses: A Tutorial. Seminars in Hearing, 43(3), 251–274. https://doi.org/10.1055/s-0042-1756219

Jeng, F.-C., Lin, T.-H., Hart, B. N., Montgomery-Reagan, K., & McDonald, K. (2022). Non-negative matrix factorization improves the efficiency of recording frequency-following responses in normal-hearing adults and neonates. International Journal of Audiology, 0(0), 1–11. https://doi.org/10.1080/14992027.2022.2071345

Karbasi, M., & Kolossa, D. (2022). ASR-based speech intelligibility prediction: A review. Hearing Research, 108606. https://doi.org/10.1016/j.heares.2022.108606

Kassjański, M., Kulawiak, M., & Przewoźny, T. (2022). Development of an AI-based audiogram classification method for patient referral. 2022 17th Conference on Computer Science and Intelligence Systems (FedCSIS), 163–168. https://doi.org/10.15439/2022F66

Law, B. M. (2022). Reimagining the Hearing Aid in an OTC Marketplace. Leader Live. https://leader.pubs.asha.org/do/10.1044/leader.FTR1.27112022.aud-otcs-future.32/full/

Lenatti, M., Moreno, -Sánchez Pedro A., Polo, E. M., Mollura, M., Barbieri, R., & Paglialonga, A. (2022). Evaluation of Machine Learning Algorithms and Explainability Techniques to Detect Hearing Loss From a Speech-in-Noise Screening Test. American Journal of Audiology, 31(3S), 961–979. https://doi.org/10.1044/2022_AJA-21-00194

Liu, Z., Li, Y., Yao, L., Monaghan, J. J. M., & McAlpine, D. (2022). Disentangled and Side-aware Unsupervised Domain Adaptation for Cross-dataset Subjective Tinnitus Diagnosis (arXiv:2205.03230). arXiv. http://arxiv.org/abs/2205.03230

López-Caballero, F., Coffman, B., Seebold, D., Teichert, T., & Salisbury, D. F. (n.d.-a). Intensity and inter-stimulus-interval effects on human middle- and long-latency auditory evoked potentials in an unpredictable auditory context. Psychophysiology, n/a(n/a), e14217. https://doi.org/10.1111/psyp.14217

Mondol, S. I. M. M. R., Kim, H. J., Kim, K. S., & Lee, S. (2022a). Machine Learning-Based Hearing Aid Fitting Personalization Using Clinical Fitting Data. Journal of Healthcare Engineering, 2022, e1667672. https://doi.org/10.1155/2022/1667672

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Neidhardt, A., Schneiderwind, C., & Klein, F. (2022). Perceptual Matching of Room Acoustics for Auditory Augmented Reality in Small Rooms—Literature Review and Theoretical Framework. Trends in Hearing, 26, 23312165221092920. https://doi.org/10.1177/23312165221092919

Orduña-Bustamante, F., Padilla-Ortiz, A. L., & Mena, C. (2023). Assessing the benefits of virtual speaker lateralization for binaural speech intelligibility over the Internet. Applied Acoustics, 202, 109146. https://doi.org/10.1016/j.apacoust.2022.109146

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Last edited January 13.

 

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