Introduction to Computational Audiology: Past, Present, and Future Applications

Introduction to Computational Audiology: Past, Present, and Future Applications

Author: Jan-Willem Wasmann, June 10, 2023.

Join a journey around the world of AI and Audiology within 80 slides! In this course about computational audiology, we embark on a journey exploring the past, present, and future of audiology and artificial intelligence. We delve into key concepts, and look at inspiring pioneers who have significantly influenced the field.

 

Above course provides an understanding of how AI can be applied in audiology, along with a balanced view of its potential risks and benefits. We will examine various AI models, with a focus on narrow AI, which is designed for specific tasks such as speech recognition. A brief history of AI is covered, starting from the definition of an algorithm to the evolution of deep learning and convolutional neural networks. The importance of high-quality data for AI systems will be underscored. The course will then explore the application of computational audiology in clinical care, looking at examples of past and ongoing projects, and contemplating the future needs and tools of the field. You will not learn how to build AI systems, but resources will be shared to further your understanding of AI. This course is tailored to innovative clinicians looking to understand and embrace AI in audiology.

References and further resources

Research Articles:

  1. Wasmann, J. W. A., Lanting, C. P., Huinck, W. J., Mylanus, E. A., van der Laak, J. W., 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, 42(6), 1499. doi: 10.1097/AUD.0000000000001041
  2. LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278-2324. DOI:10.1109/5.726791
  3. Baby, D., Van Den Broucke, A., & Verhulst, S. (2021). A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications. Nature machine intelligence, 3(2), 134-143.
  4. Barbour, D. L., Howard, R. T., Song, X. D., Metzger, N., Sukesan, K. A., DiLorenzo, J. C., Snyder, B. R. D., Chen, J. Y., Degen, E. A., Buchbinder, J. M., & Heisey, K. L. (2019). Online Machine Learning Audiometry. Ear and Hearing, 40(4), 918–926. https://doi.org/10.1097/AUD.0000000000000669
  5. Cox, M., & de Vries, B. (2021). Bayesian Pure-Tone Audiometry Through Active Learning Under Informed Priors. Frontiers in Digital Health, 3, 107. https://doi.org/10.3389/fdgth.2021.723348
  6. Kell, A. J., Yamins, D. L., Shook, E. N., Norman-Haignere, S. V., & McDermott, J. H. (2018). A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy. Neuron, 98(3), 630-644.
  7. Schlittenlacher, J., Turner, R. E., & Moore, B. C. J. (2018). Audiogram estimation using Bayesian active learning. The Journal of the Acoustical Society of America, 144(1), 421–430. https://doi.org/10.1121/1.5047436
  8. Almufarrij, I., Dillon, H., Dawes, P., Moore, D. R., Yeung, W., Charalambous, A. P., … & Munro, K. J. (2022). Web-and app-based tools for remote hearing assessment: a scoping review. International Journal of Audiology, 1-14.
  9. Wasmann, J. W., Pragt, L., Eikelboom, R., & Swanepoel, D. W. (2022). Digital approaches to automated and machine learning assessments of hearing: scoping review. Journal of medical Internet research, 24(2), e32581.
  10. Healy, E. W., Johnson, E. M., Pandey, A., & Wang, D. (2023). Progress made in the efficacy and viability of deep-learning-based noise reduction. The Journal of the Acoustical Society of America, 153(5), 2751-2751.
  11. Vickers, D., Salorio-Corbetto, M., Driver, S., Rocca, C., Levtov, Y., Sum, K., … & Picinali, L. (2021). Involving Children and Teenagers With Bilateral Cochlear Implants in the Design of the BEARS (Both EARS) Virtual Reality Training Suite Improves Personalization. Frontiers in Digital Health, 3.
  12. Roßbach, J., Kollmeier, B., & Meyer, B. T. (2022). A model of speech recognition for hearing-impaired listeners based on deep learning. The Journal of the Acoustical Society of America, 151(3), 1417–1427. https://doi.org/10.1121/10.0009411
  13. Swanepoel, D. W., Manchaiah, V., & Wasmann, J. W. A. (2023). The Rise of AI Chatbots in Hearing Health Care. The Hearing Journal, 76(04), 26-30.

Online Articles and Websites:

  1. Algorithm – Wikipedia
  2. Artificial General Intelligence – Wikipedia
  3. wav2vec 2.0 Base
  4. MNIST Database
  5. ImageNet
  6. Audiology in the Age of AI: How Chat GPT and Related Technologies Will Transform Hearing Healthcare
  7. Google’s AR Glasses
  8. miniGPT-4
  9. Resources for researchers and clinicians
  10. Google Cloud Blog: Learn TensorFlow and Deep Learning Without a PhD

Presentations:

  1. Presentation by Bram van Ginneken: Introduction to Deep Learning for Medical Image Analysis AI FOR HEALTH COURSE EDITION 2 – DAY 1.