| A convolutional recurrent attention model for subject-independent EEG signal analysis D Zhang, L Yao, K Chen, J Monaghan IEEE signal processing letters 26 (5), 715-719, 2019 | 180 | 2019 |
| A method to enhance the use of interaural time differences for cochlear implants in reverberant environments JJM Monaghan, BU Seeber The Journal of the Acoustical Society of America 140 (2), 1116-1129, 2016 | 29 | 2016 |
| A statistical, formant-pattern model for estimating vocal-tract length from formant frequency data FCAS Wiki | | |
| A statistical, formant-pattern model for segregating vowel type and vocal-tract length in developmental formant data RE Turner, TC Walters, JJM Monaghan, RD Patterson The Journal of the Acoustical Society of America 125 (4), 2374-2386, 2009 | 84* | 2009 |
| A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers X Zhang, L Yao, X Wang, J Monaghan, D Mcalpine, Y Zhang Journal of neural engineering 18 (3), 031002, 2021 | 421* | 2021 |
| ACOUSTICS2008/852 Continuous estimation of VTL from vowels using a linearly VTL-covariant speech feature C Feldbauer, J Monaghan, R Patterson | | |
| Assessment of cochlear implant hearing outcomes using ecological momentary assessment in both controlled and real-world settings JJ Monaghan, D Meng, M Poulos, Z Smith, J Mejia The Journal of the Acoustical Society of America 154 (4_supplement), A72-A72, 2023 | | 2023 |
| Auditory and visual cortical activity and relationship to functional speech outcomes in post-lingual cochlear implant users: a functional near-infrared spectroscopy (fNIRS) study A Fullerton, C McMahon, D Vickers, R Luke, H Hernandez-Perez, ... Annual ARO MidWinter Meeting (43rd: 2020), 60-61, 2020 | | 2020 |
| Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners JJM Monaghan, T Goehring, X Yang, F Bolner, S Wang, M Wright, ... The Journal of the Acoustical Society of America 141 (3), 1985-1998, 2017 | 35 | 2017 |
| Auditory speech processing for scale-shift covariance and its evaluation in automatic speech recognition RD Patterson, TC Walters, J Monaghan, C Feldbauer, T Irino Proceedings of 2010 IEEE International Symposium on Circuits and Systems …, 2010 | 5 | 2010 |
| Auditory, cognitive, and linguistic processing skills in individuals with hearing loss S Appaiah Konganda, M Sharma, JJ Monaghan, G Keidser, ... The Journal of the Acoustical Society of America 143 (3_Supplement), 1865-1865, 2018 | 2 | 2018 |
| Behavioural and Neurophysiological Assessment of Hearing Aid Benefits in an Adult Population with and without Hearing Loss S Appaiah-Konganda, M Sharma, R Ibrahim, JJM Monaghan, J Newall, ... Preprints, 2023 | | 2023 |
| Continuous estimation of VTL from vowels using a linearly VTL‐covariant speech feature C Feldbauer, JJ Monaghan, RD Patterson The Journal of the Acoustical Society of America 123 (5_Supplement), 3339-3339, 2008 | 3 | 2008 |
| Corrigendum to “Functional Connectivity Learning via Siamese-based SPD Matrix Representation of Brain Imaging Data”[Neural Networks 163 (2023) 272–285] Y Tang, D Chen, J Wu, W Tu, JJM Monaghan, P Sowman, D Mcalpine | 1 | 2023 |
| Cross-modal activity and relationship to functional speech outcomes in post-lingual cochlear implant users. A Fullerton, D Vickers, R Luke, H Hernandez-Perez, JJM Monaghan, ... Journal of Hearing Science 12 (1), 2022 | | 2022 |
| Cross-modal functional connectivity supports speech understanding in cochlear implant users AM Fullerton, DA Vickers, R Luke, AN Billing, D McAlpine, ... Cerebral Cortex 33 (7), 3350-3371, 2023 | 17 | 2023 |
| Dataset for Auditory inspired machine learning techniques can improve speech intelligibility and quality for hearing-impaired listeners S Bleeck, J MONAGHAN University of Southampton, 2017 | | 2017 |
| Dataset for Sensitivity to Envelope ITDs at High Modulation Rates J MONAGHAN, D McAlpine, S Bleeck University of Southampton, 2015 | | 2015 |
| Deep factor learning for accurate brain neuroimaging data analysis on discrimination for structural MRI and functional MRI H Ke, D Chen, Q Yao, Y Tang, J Wu, J Monaghan, P Sowman, ... IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2023 | 8 | 2023 |
| Deep reinforcement learning guided graph neural networks for brain network analysis X Zhao, J Wu, H Peng, A Beheshti, JJM Monaghan, D McAlpine, ... Neural Networks 154, 56-67, 2022 | 35 | 2022 |
David McAlpineMacquarie University在 mq.edu.au 的电子邮件经过验证
