Further reads - Computational Audiology

Here are examples of research projects and activities related to computational audiology:

- Sound Demos from the Laboratory for Computational Audition (MASSACHUSETTS INSTITUTE OF TECHNOLOGY)
- Computational modeling of the human auditory periphery: Auditory nerve responses, evoked potentials and hearing loss, developed by the Hearing Technology Lab by Prof. Verhulst

Dennis Barbour on ARO Symposium Forum
marzieh.golabbakhsh on ARO Symposium Forum
Jan-Willem Wasmann on DIN-test DEMO
Lucas Mens on DIN-test DEMO
Sigrid Polspoel on Aladdin: Automatic LAnguage-independent Development of the Digits-In-Noise test

Barbour, D. L., & Wasmann, J.-W. A. (2021). Performance and Potential of Machine Learning Audiometry. The Hearing Journal, 74(3), 40–43. https://doi.org/10.1097/01.HJ.0000737592.24476.88

Kayser, H., Herzke, T., Maanen, P., Zimmermann, M., Grimm, G., & Hohmann, V. (2021). Open community platform for hearing aid algorithm research: open Master Hearing Aid (openMHA). ArXiv:2103.02313 [Cs, Eess]. https://doi.org/10.5281/zenodo.4601604

Melgarejo-Nagata, H., & Cabanillas-Carbonell, M. (n.d.). Analysis of emerging technologies for the social inclusion of people with hearing disabilities: a review of the scientific literature from 2005-2020. European Journal of Molecular & Clinical Medicine, 8(03), 2021.

Melgarejo-Nagata, H., & Cabanillas-Carbonell, M. (2021). Analysis of emerging technologies for the social inclusion of people with hearing disabilities: a review of the scientific literature from 2005 - 2020. Clinical Medicine, 08(03), 14.

Zhang, Q., Wang, D., Zhao, R., & Yu, Y. (2021). SoundLip: Enabling Word and Sentence-level Lip Interaction for Smart Devices. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 5(1), 43:1–43:28. https://doi.org/10.1145/3448087

Shekar, R. C. M. C., Belitz, C., & Hansen, J. H. L. (2021). Development of CNN-Based Cochlear Implant and Normal Hearing Sound Recognition Models Using Natural and Auralized Environmental Audio. 2021 IEEE Spoken Language Technology Workshop (SLT), 728–733. https://doi.org/10.1109/SLT48900.2021.9383550

Ratnanather, J. T., Bhattacharya, R., Heston, M. B., Song, J., Fernandez, L. R., Lim, H. S., Lee, S.-W., Tam, E., Yoo, S., Bae, S.-H., Lam, I., Jeon, H. W., Chang, S. A., & Koo, J.-W. (2021). An mHealth App (Speech Banana) for Auditory Training: App Design and Development Study. JMIR MHealth and UHealth, 9(3), e20890. https://doi.org/10.2196/20890

Sundgaard, J. V., Harte, J., Bray, P., Laugesen, S., Kamide, Y., Tanaka, C., Paulsen, R. R., & Christensen, A. N. (2021). Deep metric learning for otitis media classification. Medical Image Analysis, 102034. https://doi.org/10.1016/j.media.2021.102034

Pitathawatchai, P., Chaichulee, S., & Kirtsreesakul, V. (2021). Robust machine learning method for imputing missing values in audiograms collected in children. International Journal of Audiology, 0(0), 1–12. https://doi.org/10.1080/14992027.2021.1884909

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

Performance and Potential of Machine Learning Audiometry : The Hearing Journal. (n.d.). Retrieved April 5, 2021, from https://journals.lww.com/thehearingjournal/Fulltext/2021/03000/Performance_and_Potential_of_Machine_Learning.11.aspx

Hülsmeier, D., Schädler, M. R., & Kollmeier, B. (2021). DARF: A data-reduced FADE version for simulations of speech recognition thresholds with real hearing aids. Hearing Research, 404, 108217. https://doi.org/10.1016/j.heares.2021.108217

Jones, S. A., & Noppeney, U. (2021). Ageing and multisensory integration: A review of the evidence, and a computational perspective. Cortex, 138, 1–23. https://doi.org/10.1016/j.cortex.2021.02.001

Kim, S. (n.d.). AI-Driven Insights from AI-Driven Data - Hearing Review. Retrieved April 5, 2021, from https://www.hearingreview.com/hearing-products/hearing-aids/ai

Uhler, K., Hunter, S., & Gilley, P. M. (2021). Mismatched response predicts behavioral speech discrimination outcomes in infants with hearing loss and normal hearing. Infancy, 26(2), 327–348. https://doi.org/https://doi.org/10.1111/infa.12386

Wimalarathna, H., Ankmnal-Veeranna, S., Allan, C., Agrawal, S. K., Allen, P., Samarabandu, J., & Ladak, H. M. (2021). Comparison of machine learning models to classify Auditory Brainstem Responses recorded from children with Auditory Processing Disorder. Computer Methods and Programs in Biomedicine, 200, 105942. https://doi.org/10.1016/j.cmpb.2021.105942

Dotan, A., & Shriki, O. (2021). Tinnitus-like “hallucinations” elicited by sensory deprivation in an entropy maximization recurrent neural network. BioRxiv, 2021.01.11.426188. https://doi.org/10.1101/2021.01.11.426188

Dotan, A., & Shriki, O. (2021). Tinnitus-like “hallucinations” elicited by sensory deprivation in an entropy maximization recurrent neural network. BioRxiv.

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

Headphone screening to facilitate web-based auditory experiments | SpringerLink. (n.d.). Retrieved March 14, 2021, from https://link.springer.com/article/10.3758/s13414-017-1361-2

Woods, K. J. P., Siegel, M. H., Traer, J., & McDermott, J. H. (2017). Headphone screening to facilitate web-based auditory experiments. Attention, Perception, & Psychophysics, 79(7), 2064–2072. https://doi.org/10.3758/s13414-017-1361-2

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 Hearing, 41(2), 442–450. https://doi.org/10.1097/AUD.0000000000000775

Chesley, B., & Barbour, D. L. (2020). Visual Field Estimation by Probabilistic Classification. IEEE Journal of Biomedical and Health Informatics, 24(12), 3499–3506. https://doi.org/10.1109/JBHI.2020.2999567

Motlagh Zadeh, L., Silbert, N. H., Sternasty, K., Swanepoel, D. W., Hunter, L. L., & Moore, D. R. (2019). Extended high-frequency hearing enhances speech perception in noise. Proceedings of the National Academy of Sciences, 116(47), 23753–23759. https://doi.org/https://doi.org/10.1073/pnas.1903315116

Saunders, G. H., Bott, A., & Tietz, L. H. (2020). Hearing care providers’ perspectives on the utility of datalogging information. American Journal of Audiology, 29(3S), 610–622. https://doi.org/https://doi.org/10.1044/2020_AJA-19-00089

Saunders Gabrielle H., Bott Anthea, & Tietz Lukas H. B. (2020). Hearing Care Providers’ Perspectives on the Utility of Datalogging Information. American Journal of Audiology, 29(3S), 610–622. https://doi.org/10.1044/2020_AJA-19-00089

Schmidt, J. H., Brandt, C., Pedersen, E. R., Christensen-Dalsgaard, J., Andersen, T., Poulsen, T., & Bælum, J. (2014). A user-operated audiometry method based on the maximum likelihood principle and the two-alternative forced-choice paradigm. International Journal of Audiology, 53(6), 383–391.

Masalski, M., Kipiński, L., Grysiński, T., & Kręcicki, T. (2016). Hearing tests on mobile devices: evaluation of the reference sound level by means of biological calibration. Journal of Medical Internet Research, 18(5), e130.

Masalski, M., Grysiński, T., & Kręcicki, T. (2018). Hearing tests based on biologically calibrated mobile devices: comparison with pure-tone audiometry. JMIR MHealth and UHealth, 6(1), e10.

Colsman, A., Supp, G. G., Neumann, J., & Schneider, T. R. (2020). Evaluation of Accuracy and Reliability of a Mobile Screening Audiometer in Normal Hearing Adults. Frontiers in Psychology, 11, 744.

Yao, J., Wan, Y., & Givens, G. D. (2010). Using web services to realize remote hearing assessment. Journal of Clinical Monitoring and Computing, 24(1), 41–50.

Svensson, U. P., Kvaløy, O., & Berg, T. (2015). A comparison of test–retest variability and time efficiency of auditory thresholds measured with pure tone audiometry and new early warning test. Applied Acoustics, 90, 153–159.

Corry, M., Sanders, M., & Searchfield, G. D. (2017). The accuracy and reliability of an app-based audiometer using consumer headphones: pure tone audiometry in a normal hearing group. International Journal of Audiology, 56(9), 706–710.

Margolis, R. H., Glasberg, B. R., Creeke, S., & Moore, B. C. (2010). AMTAS®: Automated method for testing auditory sensitivity: Validation studies. International Journal of Audiology, 49(3), 185–194.

Margolis, R. H., & Moore, B. C. (2011). AMTAS®: Automated method for testing auditory sensitivity: III. Sensorineural hearing loss and air-bone gaps. International Journal of Audiology, 50(7), 440–447.

Margolis, R. H., Frisina, R., & Walton, J. P. (2011). AMTAS®: Automated method for testing auditory sensitivity: II. Air conduction audiograms in children and adults. International Journal of Audiology, 50(7), 434–439.

Sandström, J., Swanepoel, D., Laurent, C., Umefjord, G., & Lundberg, T. (2020). Accuracy and reliability of smartphone self-test audiometry in community clinics in low income settings: a comparative study. Annals of Otology, Rhinology & Laryngology, 129(6), 578–584.

Bornman, M., Swanepoel, D. W., De Jager, L. B., & Eikelboom, R. H. (2019). Extended high-frequency smartphone audiometry: validity and reliability. Journal of the American Academy of Audiology, 30(3), 217–226.

Sandström, J., Swanepoel, D. W., Carel Myburgh, H., & Laurent, C. (2016). Smartphone threshold audiometry in underserved primary health-care contexts. International Journal of Audiology, 55(4), 232–238.

Yeung, J., Javidnia, H., Heley, S., Beauregard, Y., Champagne, S., & Bromwich, M. (2013). The new age of play audiometry: prospective validation testing of an iPad-based play audiometer. J Otolaryngol Head Neck Surg, 42(1), 21.

Yao, J. J., Yao, D., & Givens, G. (2015). A Browser-Server-Based Tele-audiology System That Supports Multiple Hearing Test Modalities. Telemed J E Health, 21(9), 697–704.

Whitton, J. P., Hancock, K. E., Shannon, J. M., & Polley, D. B. (2016). Validation of a Self-Administered Audiometry Application: An Equivalence Study. Laryngoscope, 126(10), 2382–2388.

van Tonder, J., Swanepoel, W., Mahomed-Asmail, F., Myburgh, H., & Eikelboom, R. H. (2017). Automated Smartphone Threshold Audiometry: Validity and Time Efficiency. J Am Acad Audiol, 28(3), 200–208.

Yeung, J. C., Heley, S., Beauregard, Y., Champagne, S., & Bromwich, M. A. (2015). Self-administered hearing loss screening using an interactive, tablet play audiometer with ear bud headphones. Int J Pediatr Otorhinolaryngol, 79(8), 1248–1252.

Vinay, Henriksen, V., Kvaloy, O., & Svensson, U. P. (2014). Development and Calibration of a New Automated Method to Measure Air Conduction Auditory Thresholds Using an Active Earplug. Acta Acustica United with Acustica, 100(1), 113–117. ://WOS:000330557200013

Van Tasell, D. J., & Folkeard, P. (2013). Reliability and accuracy of a method of adjustment for self-measurement of auditory thresholds. Otol Neurotol, 34(1), 9–15.

Thoidis, I., Vrysis, L., Markou, K., & Papanikolaou, G. (2019). Development and evaluation of a tablet-based diagnostic audiometer. Int J Audiol, 58(8), 476–483.

Swanepoel de, W., Matthysen, C., Eikelboom, R. H., Clark, J. L., & Hall, J. W., 3rd. (2015). Pure-tone audiometry outside a sound booth using earphone attentuation, integrated noise monitoring, and automation. Int J Audiol, 54(11), 777–785.