Newsroom Computational Audiology, November 5
Below you find the latest news and developments in computational audiology: biomimetic cochleas, computational audiology discussion group, and an overview of 2021 third quarter’s publications related to computational audiology.
The Cambridge Sense lab and Biointerfaces lab produced and studied biomimetic cochleas to understand the cochlear implant-human biointerface using machine learning (Lei et al., 2021)
CAN reading group
Starting this week, Stephany Folwer is moderating a reading group that follows the online video series AI For Everyone by Andrew Ng of Stanford (available here on Coursera), interested CAN members can join a discussion group on that topic on a set day within the Slack #readinggroup channel. These discussions are intended to clarify topics learned during that week’s lesson, as well as come up with applicable ways to consider these topics within your own lab or across labs and specialties. More info you can still join this week.
Recent publications related to computational audiology
Did we miss a publication? Please send your suggestion to firstname.lastname@example.org
Below publications were found using [(Computational Audiology) OR ((Machine Learning) AND audiology)] in Google Scholar.
Hu et al. (2021) describe a computational model for tinnitus subjects based on the Bayesian brain concept. While Tarnowska et al. (2021) propose a data-driven clinical decision support system for the diagnosis and therapy of hearing disorders including tinnitus. In an extensive critical review, Carlyon & Goehring (2021) assess Cochlear Implant research of the last 20 years and identify promising areas of future developments. Lesica et al. (2021) describe opportunities to apply AI in hearing health care and make a call for the AI and hearing communities to come together to bring about a technological revolution in hearing.
These and many more developments are published below.
Bibliography Q3 2021
Abu Bakar, A. R., Lai, K. W., & Hamzaid, N. A. (2021). The emergence of machine learning in auditory neural impairment: A systematic review. Neuroscience Letters, 765, 136250. https://doi.org/10.1016/j.neulet.2021.136250
Balling, L. W., Mølgaard, L. L., Townend, O., & Nielsen, J. B. B. (2021). The Collaboration between Hearing Aid Users and Artificial Intelligence to Optimize Sound. Seminars in Hearing, 42(3), 282–294. https://doi.org/10.1055/s-0041-1735135
Bhowmik, A. K., Fabry, D. A., Armour, P., Berghel, H., Charette, R. N., & King, J. L. (2021). Hear, Now, and in the Future: Transforming Hearing Aids Into Multipurpose Devices. Computer, 54(11), 108–120. https://doi.org/10.1109/MC.2021.3105151
Brennan, R. L., Steffler, S., Dods, J., & He, J. (2021). Hearing aid and Extreme Edge IoT Acceleration. 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), 684–687. https://doi.org/10.1109/MWSCAS47672.2021.9531923
Calado, A., Errico, V., & Saggio, G. (2021). Toward the Minimum Number of Wearables to Recognize Signer-Independent Italian Sign Language With Machine-Learning Algorithms. IEEE Transactions on Instrumentation and Measurement, 70, 1–9. https://doi.org/10.1109/TIM.2021.3109732
Carlyon, R. P., & Goehring, T. (2021). Cochlear Implant Research and Development in the Twenty-first Century: A Critical Update. Journal of the Association for Research in Otolaryngology, 22(5), 481–508. https://doi.org/10.1007/s10162-021-00811-5
Drgas, S., Blaszak, M., & Przekoracka, -Krawczyk Anna. (2021). The Combination of Neural Tracking and Alpha Power Lateralization for Auditory Attention Detection. Journal of Speech, Language, and Hearing Research, 64(9), 3603–3616. https://doi.org/10.1044/2021_JSLHR-20-00608
Fabry, D. A., & Bhowmik, A. K. (2021). Improving Speech Understanding and Monitoring Health with Hearing Aids Using Artificial Intelligence and Embedded Sensors. Seminars in Hearing, 42(3), 295–308. https://doi.org/10.1055/s-0041-1735136
Feng, Y., & Chen, F. (2022). Nonintrusive objective measurement of speech intelligibility: A review of methodology. Biomedical Signal Processing and Control, 71, 103204. https://doi.org/10.1016/j.bspc.2021.103204
Ferlini, A., Ma, D., Harle, R., & Mascolo, C. (2021). EarGate: Gait-based User Identification with In-ear Microphones. Proceedings of the 27th Annual International Conference on Mobile Computing and Networking, 337–349. https://doi.org/10.1145/3447993.3483240
Gao, X., Grayden, D., & McDonnell, M. (2021). Unifying information theory and machine learning in a model of electrode discrimination in cochlear implants. PLOS ONE, 16(9), e0257568. https://doi.org/10.1371/journal.pone.0257568
Hafeez, N., Du, X., Boulgouris, N., Begg, P., Irving, R., Coulson, C., & Tourrel, G. (2021). Electrical impedance guides electrode array in cochlear implantation using machine learning and robotic feeder. Hearing Research, 412, 108371. https://doi.org/10.1016/j.heares.2021.108371
Hajizadeh, A., Matysiak, A., Wolfrum, M., & Patrick J. C. May, P. J. C. (2021). Auditory cortex modelled as a dynamical network of oscillators: Understanding event-related fields and their adaptation [PDF]. https://doi.org/10.20347/WIAS.PREPRINT.2854
Hu, S., Hall, D. A., Zubler, F., Sznitman, R., Anschuetz, L., Caversaccio, M., & Wimmer, W. (2021). Bayesian brain in tinnitus: Computational modeling of three perceptual phenomena using a modified Hierarchical Gaussian Filter. Hearing Research, 410, 108338. https://doi.org/10.1016/j.heares.2021.108338
Keshavarzi, M., Reichenbach, T., & Moore, B. C. J. (2021). Transient Noise Reduction Using a Deep Recurrent Neural Network: Effects on Subjective Speech Intelligibility and Listening Comfort. Trends in Hearing, 25, 23312165211041476. https://doi.org/10.1177/23312165211041475
Korhonen, P. (2021). Wind Noise Management in Hearing Aids. Seminars in Hearing, 42(3), 248–259. https://doi.org/10.1055/s-0041-1735133
Kyong, J.-S., Suh, M.-W., Han, J. J., Park, M. K., Noh, T. S., Oh, S. H., & Lee, J. H. (2021). Cross-Modal Cortical Activity in the Brain Can Predict Cochlear Implantation Outcome in Adults: A Machine Learning Study. The Journal of International Advanced Otology, 17(5), 380–386. https://doi.org/10.5152/iao.2021.9337
Lei, I. M., Jiang, C., Lei, C. L., de Rijk, S. R., Tam, Y. C., Swords, C., Sutcliffe, M. P. F., Malliaras, G. G., Bance, M., & Huang, Y. Y. S. (2021). 3D printed biomimetic cochleae and machine learning co-modelling provides clinical informatics for cochlear implant patients. Nature Communications, 12(1), 6260. https://doi.org/10.1038/s41467-021-26491-6
Lesica, N. A., Mehta, N., Manjaly, J. G., Deng, L., Wilson, B. S., & Zeng, F.-G. (2021). Harnessing the power of artificial intelligence to transform hearing healthcare and research. Nature Machine Intelligence, 3(10), 840–849. https://doi.org/10.1038/s42256-021-00394-z
Li, B. (2021). Hearing loss classification via AlexNet and extreme learning machine. International Journal of Cognitive Computing in Engineering, 2, 144–153. https://doi.org/10.1016/j.ijcce.2021.09.002
McLachlan, G., Majdak, P., Reijniers, J., & Peremans, H. (2021). Towards modelling active sound localisation based on Bayesian inference in a static environment. Acta Acustica, 5, 45. https://doi.org/10.1051/aacus/2021039
Nayak, S., Kumar, C. S., & Murty, K. S. R. (2021). Instantaneous frequency filter-bank features for low resource speech recognition using deep recurrent architectures. 2021 National Conference on Communications (NCC), 1–6. https://doi.org/10.1109/NCC52529.2021.9530049
Occelli, F., Hasselmann, F., Bourien, J., Puel, J.-L., Desvignes, N., Wiszniowski, B., EDELINE, J.-M., & Gourévitch, B. (2021). Temporal alterations to central auditory processing without synaptopathy after lifetime exposure to environmental noise. Cerebral Cortex, bhab310. https://doi.org/10.1093/cercor/bhab310
Tarnowska, K. A., Dispoto, B. C., & Conragan, J. (2021). Explainable AI-based clinical decision support system for hearing disorders. AMIA Summits on Translational Science Proceedings, 2021, 595–604.
Wong, P. C. M., Lai, C. M., Chan, P. H. Y., Leung, T. F., Lam, H. S., Feng, G., Maggu, A. R., & Novitskiy, N. (2021). Neural Speech Encoding in Infancy Predicts Future Language and Communication Difficulties. American Journal of Speech-Language Pathology, 30(5), 2241–2250. https://doi.org/10.1044/2021_AJSLP-21-00077
Yang, T., Liu, Q., Fan, X., Hou, B., Wang, J., & Chen, X. (2021). Altered regional activity and connectivity of functional brain networks in congenital unilateral conductive hearing loss. NeuroImage: Clinical, 32, 102819. https://doi.org/10.1016/j.nicl.2021.102819
Zanet, M., Polo, E. M., Lenatti, M., Van Waterschoot, T., Mongelli, M., Barbieri, R., & Paglialonga, A. (2021). Evaluation of a Novel Speech-in-Noise Test for Hearing Screening: Classification Performance and Transducers Characteristics. IEEE Journal of Biomedical and Health Informatics, 1–1. https://doi.org/10.1109/JBHI.2021.3100368
Last edited Nov 5.