2022
Robust and interpretable temporal convolution network for event detection in lung sound recordings
IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 7, pp. 2898–2908
T. Fernando, S. Sridharan, S. Denman, H. Ghaemmaghami, C. Fookes
Heart and lungs
Segmentation, classifiers that explain themselves, and models that generalise to microphones and patients they have not seen.
2020
Domain generalization in biosignal classification
IEEE Transactions on Biomedical Engineering, vol. 68, no. 6, pp. 1978–1989
T. Dissanayake, T. Fernando, S. Denman, H. Ghaemmaghami, S. Sridharan, C. Fookes
2020
A robust interpretable deep learning classifier for heart anomaly detection without segmentation
IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 6, pp. 2162–2171
T. Dissanayake, T. Fernando, S. Denman, S. Sridharan, H. Ghaemmaghami, C. Fookes
2019
Heart sound segmentation using bidirectional LSTMs with attention
IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 6, pp. 1601–1609
T. Fernando, H. Ghaemmaghami, S. Denman, S. Sridharan, N. Hussain, C. Fookes
2017
Automatic segmentation and classification of cardiac cycles using deep learning and a wireless electronic stethoscope
IEEE Life Sciences Conference, pp. 210–213
H. Ghaemmaghami, N. Hussain, K. Tran, A. Carey, S. Hussain, F. Syed, A. J. Sinskey, K. O'Hashi, J. Sperling
Sleep
Snoring is a body sound too. This earlier work asked whether its statistics could point to obstructive sleep apnoea.
2010
Multi-parametric snore analysis on OSA diagnosis
Sleep Down Under 2010, 22nd Annual Scientific Meeting of the Australasian Sleep Association
U. R. Abeyratne, C. Hukins, V. Swarnkar, S. Karunajeeva, S. De Silva, H. Ghaemmaghami
2009
Normal probability testing of snore signals for diagnosis of obstructive sleep apnea
IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5551–5554, Minneapolis
H. Ghaemmaghami, U. R. Abeyratne, C. Hukins
2009
The utility of the analysis of the gaussianity of snore related sounds in the diagnosis of obstructive sleep apnea
Journal of Sleep and Biological Rhythms, vol. 7, no. 1, p. A27
H. Ghaemmaghami, U. Abeyratne, C. Hukins, B. Duce
Voice and robust audio
Finding a faint signal in noise is the same problem whether the signal is speech or a heartbeat. This work is where the robustness came from.
2017
A study on the effects of using short utterance length development data in the design of GPLDA speaker verification systems
International Journal of Speech Technology, vol. 20, no. 2, pp. 247–259
A. Kanagasundaram, D. Dean, S. Sridharan, H. Ghaemmaghami, C. Fookes
2016
A study of speaker clustering for speaker attribution in large telephone conversation datasets
Computer Speech & Language, vol. 40, pp. 23–45
H. Ghaemmaghami, D. Dean, S. Sridharan, D. A. van Leeuwen
2015
Complete-linkage clustering for voice activity detection in audio and visual speech
Interspeech 2015
H. Ghaemmaghami, D. Dean, S. Kalantari, S. Sridharan, C. Fookes
2015
Acoustic adaptation in cross database audio visual SHMM training for phonetic spoken term detection
Third Workshop on Speech, Language & Audio in Multimedia, pp. 11–14
S. Kalantari, D. Dean, S. Sridharan, H. Ghaemmaghami, C. Fookes
2010
Noise robust voice activity detection using features extracted from the time-domain autocorrelation function
Interspeech 2010, pp. 3118–3121
H. Ghaemmaghami, B. Baker, R. Vogt, S. Sridharan
2010
Noise robust voice activity detection using normal probability testing and time-domain histogram analysis
IEEE ICASSP 2010, pp. 4470–4473
H. Ghaemmaghami, D. Dean, S. Sridharan, I. McCowan
Citations as listed by the authors. For reprints, write to info@beataware.com.