• 212 Citations
  • 6 h-Index
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Personal profile


Jessica Monaghan started her scientific career reading Natural Sciences at the University of Cambridge, specialising in Physics. There she completely her Masters research project into how the principles of human listening might be applied to improve automatic speech recognition by machines at the Centre for the Neural Basis of Hearing (CNBH). After working at the CNBH for a year as a research assistant, she undertook her PhD studies at the UK Medical Research Council’s Institute for Hearing Research, where she developed an algorithm to improve the ability of cochlear implant users to localise sounds in rooms. As a postdoctoral researcher at the University of Southampton, she developed noise-reduction algorithms for hearing aids and cochlear implants using state-of-the-art machine learning techniques such as neural network and sparse coding. In 2015 she moved to Australia to work as a Research Fellow at Macquarie University, where she is researching the ability of people with normal hearing to understand speech in noisy environments, and using this knowledge to develop the next generation of hearing devices. 


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Projects 2017 2021

Speech Enhancement in Humans and Machines

Monaghan, J., Goehring, T., Bleeck, S. & McMahon, C.

13/03/17 → …

Project: Research

Research Outputs 2008 2018

  • 212 Citations
  • 6 h-Index
  • 10 Article
  • 4 Conference proceeding contribution
  • 2 Meeting abstract

Auditory, cognitive, and linguistic processing skills in individuals with hearing loss

Appaiah Konganda, S., Sharma, M., Monaghan, J. J., Keidser, G., Valderrama Valenzuela, J. T., Newall, J. & Beach, E., 2018, In : Journal of the Acoustical Society of America. 143, 3, p. 1865 1 p.

Research output: Contribution to journalMeeting abstractResearch

Hearing Loss

Tolerable delay for speech production and perception: effects of hearing ability and experience with hearing aids

Goehring, T., Chapman, J. L., Bleeck, S. & Monaghan, J. J. M., 2018, In : International Journal of Audiology. 57, 1, p. 61-68 8 p.

Research output: Contribution to journalArticleResearchpeer-review

Speech Perception
Hearing Aids
Hearing Loss

Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners

Monaghan, J. J. M., Goehring, T., Yang, X., Bolner, F., Wang, S., Wright, M. C. M. & Bleeck, S., Mar 2017, In : Journal of the Acoustical Society of America. 141, 3, p. 1985-1998 14 p.

Research output: Contribution to journalArticleResearchpeer-review

machine learning

Influence of envelope waveform on ITD sensitivity of neurons in the auditory midbrain

Greenberg, D., Monaghan, J. J. M., Dietz, M., Marquardt, T. & McAlpine, D., 17 Oct 2017, In : Journal of Neurophysiology. 118, 4, p. 2358-2370 13 p.

Research output: Contribution to journalArticleResearchpeer-review


Speech enhancement based on neural networks improves speech intelligibility in noise for cochlear implant users

Goehring, T., Bolner, F., Monaghan, J. J. M., van Dijk, B., Zarowski, A. & Bleeck, S., Feb 2017, In : Hearing Research. 344, p. 183-194 12 p.

Research output: Contribution to journalArticleResearchpeer-review

Open Access
Speech Intelligibility
Cochlear Implants
Signal-To-Noise Ratio
Electric Stimulation