Mike Brookes is a Reader in the Communications and Signal Processing group. After gaining a Mathematics degree from Cambridge, he spent four years at MIT and, later, the University of Hawaii working on the development of telescope systems and astronomical instrumentation. In 1975 he returned to the UK and joined the EEE Dept at Imperial College.
Mike's reseach focuses on speech processing and computer vision. Within the area of speech processing, he has concentrated on the modelling and analysis of speech signals, the extraction of features for speech and speaker recognition and, most recently, on the enhancement of poor quality speech signals. Between 2007 and 2012, he was the Director of the Home Office sponsored Centre for Law Enforcement Audio Research (CLEAR) which investigated techniques for processing heavily corrupted speech signals. In computer vision he led a successful MoD-funded project on radar target recognition in partnership with General Dynamics and Qinetiq and is currently working on a number of computer vision projects concerned with change detection and image-based rendering from multiperspective camera systems.
Mike's teaches courses in mathematics, analysis of circuits, digital signal processing and digital filters.
For more information see http://www.ee.ic.ac.uk/hp/staff/dmb/dmb.html
et al., 2019, Noise covariance matrix estimation for rotating microphone arrays, Ieee/acm Transactions on Audio, Speech and Language Processing, Vol:27, ISSN:2329-9290, Pages:519-530
Wang Y, Brookes DM, 2017, Model-Based Speech Enhancement in the Modulation Domain, Ieee/acm Transactions on Audio, Speech and Language Processing, Vol:26, ISSN:2329-9304, Pages:580-594
Doire CSJ, Brookes DM, Naylor PA, 2017, Robust and efficient Bayesian adaptive psychometric function estimation, Journal of the Acoustical Society of America, Vol:141, ISSN:0001-4966, Pages:2501-2512
et al., 2017, Single-channel online enhancement of speech corrupted by reverberation and noise, Ieee/acm Transactions on Audio, Speech and Language Processing, Vol:25, ISSN:2329-9290, Pages:572-587
Gilliam C, Dragotti P-L, Brookes M, 2014, On the Spectrum of the Plenoptic Function, IEEE Transactions on Image Processing, Vol:23, ISSN:1057-7149, Pages:502-516
et al., 2014, Effects of noise suppression on intelligibility. II: An attempt to validate physical metrics, Journal of the Acoustical Society of America, Vol:135, ISSN:0001-4966, Pages:439-450
Gaubitch N, Brookes M, Naylor P, 2013, Blind Channel Magnitude Response Estimation in Speech using Spectrum Classification, IEEE Transactions on Audio Speech and Language Processing, Vol:21, ISSN:1558-7916, Pages:2162-2171
Pearson J, Brookes M, Dragotti P-L, 2013, Plenoptic layer-based modelling for image based rendering, IEEE Transactions on Image Processing, Vol:22, ISSN:1057-7149, Pages:3405-3419
et al., 2012, Effects of noise suppression on intelligibility: dependency on signal-to-noise ratios, Journal of the Acoustical Society of America, Vol:131, Pages:531-539
Bouganis C-S, Brookes M, 2007, Statistical Multiple Light Source Detection, Iet Computer Vision, Vol:1, Pages:79-91
et al., 2007, Estimation of glottal closure instants in voiced speech using the DYPSA algorithm, IEEE Transactions on Audio Speech and Language Processing, Vol:15, ISSN:1558-7916, Pages:34-43
Brookes, M., Naylor, P.A., Gudnason, J., 2006, A quantitative assessment of group delay methods for identifying glottal closures in voiced speech, IEEE Transactions on Audio Speech and Language Processing, Vol:14, ISSN:1558-7916, Pages:456-466