Prof. Haizhou Li

IEEE Fellow, ISCA Fellow

National University of Singapore, Singapore

Haizhou Li is a Presidential Chair Professor and Associate Dean (Research) at the School of Data Science, The Chinese University of Hong Kong, Shenzhen, China. Dr. Li is also with the Department of Electrical and Computer Engineering, National University of Singapore (NUS), Singapore. Dr. Li has worked on speech and language technology in academia and industry since 1988. He has taught in The University of Hong Kong (1988-1989), South China University of Technology in Guangzhou, China (1990-1994), Nanyang Technological University in Singapore (2006-2016), University of Eastern Finland (2009), and University of New South Wales (since 2011). He was a Visiting Professor at CRIN/INRIA in France (1994-1995). Prior to joining CUHKSZ and NUS, he was a Research Manager in Apple-ISS Research Centre (1996-1998), Research Director of Lernout & Hauspie Asia Pacific (1999-2001), Vice President of InfoTalk Corp. Ltd and General Manager of InfoTalk Technology (Singapore) Pte Ltd (2001-2003), the Principal Scientist and Department Head of Human Language Technology at the Institute for Infocomm Research (2003-2016), and the Research Director of the Institute for Infocomm Research (2014-2016), the Agency for Science, Technology and Research, Singapore. He co-founded Baidu-I2R Research Centre in Singapore (2012). Dr. Li was known for his technical contributions to several award-winning speech products, such as Apple's Chinese Dictation Kits for Macintosh (1996) and Lernout & Hauspie's Speech-Pen-Keyboard Text Entry Solution for Asian languages (1999). He was the architect of a series of major technology deployments that include TELEFIQS voice-automated call centre service in Singapore Changi International Airport (2001), voiceprint engine for Lenovo A586 Smartphone (2012), and Baidu Music Search (2013). Dr. Li was the recipient of National Infocomm Awards 2002, Institution of Engineers Singapore (IES) Prestigious Engineering Achievement Award 2013 and 2015, President's Technology Award 2013, and MTI Innovation Activist Gold Award 2015 in Singapore. He was named one of the two Nokia Visiting Professors in 2009 by Nokia Foundation, IEEE Fellow in 2014 for leadership in multilingual, speaker and language recognition, ISCA Fellow in 2018 for contributions to multilingual speech information processing, and Bremen Excellence Chair Professor in 2019. Dr. Li is a member of ACL, ACM, and APSIPA.

Abstract: Humans have a remarkable ability to pay their auditory attention only to a sound source of interest, that we call selective auditory attention, in a multi-talker environment or a Cocktail Party. However, signal processing approach to speech separation and/or speaker extraction from multi-talker speech remains a challenge for machines. In this talk, we study the deep learning solutions to monaural speech separation and speaker extraction that enable selective auditory attention. We review the findings from human audio-visual speech perception to motivate the design of speech perception algorithms. We introduce their applications in speech enhancement, speaker extraction, and speech recognition. We will also discuss the computational auditory models, technical challenges and the recent advances in the field.

Prof. Robert Minasian

IEEE Fellow & OSA Fellow

The University of Sydney, Australia

Professor Minasian is a Chair Professor with the School of Electrical and Information Engineering at the University of Sydney, Australia. He is also the Founding Director of the Fibre-optics and Photonics Laboratory. His research has made key contributions to microwave photonic signal processing. He is recognized as an author of one of the top 1% most highly cited papers in his field worldwide. Professor Minasian has contributed over 390 research publications, including Invited Papers in the IEEE Transactions and Journals. He has 79 Plenary, Keynote and Invited Talks at international conferences. He has served on numerous technical and steering committees of international conferences, and is on the IEEE Fellow Evaluation Committee. . Professor Minasian was the recipient of the ATERB Medal for Outstanding Investigator in Telecommunications, awarded by the Australian Telecommunications and Electronics Research Board. He is a Life Fellow of the IEEE, and a Fellow of the Optical Society of America.

Abstract: Integrated photonic signal processing offers new powerful paradigms for signal processing systems. This stems from its inherent advantages of wide bandwidth and immunity to electromagnetic interference. Microwave photonics, which merges the worlds of RF and photonics, shows strong potential as a key enabling technology to provide new signal processing systems that can overcome inherent electronic limitations. Current trends are focused on the integration of photonics on silicon platforms that leverage highly developed CMOS fabrication technologies to enable boosting the performance of future systems performing signal processing and deep learning, with the potential for implementing high bandwidth, fast and complex functionalities. Recent progress in silicon photonics integrated signal processing is presented. This includes LIDAR-on-a-chip systems, photonic approaches to artificial neural networks for deep learning, multi-function signal processors, programmable integrated photonic processors, and high-resolution integrated sensors. These photonic processors open new capabilities for achieving high-performance signal processing.

Prof. Vivek K. Goyal

IEEE Fellow & Optica Fellow

The Boston University, USA

Dr. Goyal is a Fellow of the IEEE. He currently serves as a Senior Area Editor of IEEE Transactions on Computational Imaging, as an Associate Editor of SIAM Journal on Imaging Sciences, and on the Editorial Board of Foundations and Trends and Signal Processing. He was awarded the 2002 IEEE Signal Processing Society (SPS) Magazine Award, the 2017 IEEE SPS Best Paper Award, an NSF CAREER Award, and the Best Paper Award at the 2014 IEEE International Conference on Image Processing. Work he supervised won student best paper awards at the IEEE Data Compression Conference in 2006 and 2011, the IEEE Sensor Array and Multichannel Signal Processing Workshop in 2012, and the IEEE International Conference on Imaging Processing in 2018 as well as five MIT thesis awards. He previously served on the Scientific Advisory Board of the Banff International Research Station for Mathematical Innovation and Discovery, as Technical Program Committee Co-chair of Sampling Theory and Applications 2015, and as Conference Co-chair of the SPIE Wavelets and Sparsity conference series 2006-2016. He is a co-author of Foundations of Signal Processing (Cambridge University Press, 2014).

Abstract: Single-photon detection (SPD) is no longer rare. Consumer applications include 3D imaging in autonomous vehicles, tablets, and smartphones, and scientific applications include spectroscopy, optical microscopy, and quantum optics. Single-electron detection (SED) is also possible, but it is much less common. This talk will focus on how modeling at the level of individual detected particles inspires novel processing methods. Several improvements in lidar with SPD have been developed and experimentally realized. Theory and simulations suggest that SED can lead to great improvements in particle beam microscopy.

In single-photon lidar, when detector dead times are insignificant, Poisson process models can be used directly and lead to accurate depth and reflectivity imaging with as few as one detected photon per pixel. Under high ambient light or with a high dynamic range of intensity, dead times are significant and create statistical dependencies that invalidate a Poisson process model. In this case, Markov chain modeling can mitigate the bias of conventional methods. In focused ion beam microscopy, modeling at the level of individual incident particles and detected secondary electrons inspires a new way to acquire and interpret the data. In both families of applications, old-school statistical modeling and estimation methods lead to significant imaging improvements.