Keynote Speaker Ⅰ

Assoc. Prof. Justin Dauwels

 Delft University of Technology, the Netherlands

Speech Title: AI for Applications in Psychiatry

Abstract: Many tasks in medicine still involve substantial manual work. In many cases there is strong potential for intelligent automation by A.I., leading possibly to a reduction in costs and man-hours, while increasing the quality of clinical service. In this talk, we will consider applications of A.I. in the domain of psychiatry. Specifically, we will give an overview of our research towards automated behavioral analysis for assessing the negative symptoms of mentally ill patients.

A Brief Introduction to Assoc. Prof. Justin Dauwels: Dr. Justin Dauwels is an Associate Professor at the TU Delft (Signals and Systems, Department of Microelectronics). He was an Associate Professor of the School of Electrical and Electronic Engineering at the Nanyang Technological University (NTU) in Singapore till the end of 2020. He was the Deputy Director of the ST Engineering – NTU corporate lab, which comprises 100+ PhD students, research staff and engineers, developing novel autonomous systems for airport operations and transportation.  At the TU Delft, he serves as scientific lead of the Model-Driven Decisions Lab (MoDDL), a first lab for the Knowledge Building program between the police and the TU Delft. He also serves as Chairperson of the EE Board of Studies at the TU Delft, and is a board member of the Netherlands Institute for Research on ICT. His research interests are in data analytics with applications to intelligent transportation systems, autonomous systems, and analysis of human behaviour and physiology. He obtained his PhD degree in electrical engineering at the Swiss Polytechnical Institute of Technology (ETH) in Zurich in December 2005. Moreover, he was a postdoctoral fellow at the RIKEN Brain Science Institute (2006-2007) and a research scientist at the Massachusetts Institute of Technology (2008-2010). He has been elected as IEEE SPS 2024 Distinguished Lecturer. He has been a JSPS postdoctoral fellow (2007), a BAEF fellow (2008), a Henri-Benedictus Fellow of the King Baudouin Foundation (2008), and a JSPS invited fellow (2010, 2011). He served as Chairman of the IEEE CIS Chapter in Singapore from 2018 to 2020, and served as Associate Editor of the IEEE Transactions on Signal Processing (2018 - 2023), and serves currently as Associate Editor (2021-2023) and Subject Editor (since 2023) of the Elsevier journal Signal Processing, Area Editor  for the IEEE Signal Processing Magazine (since 2023), member of the Editorial Advisory Board of the International Journal of Neural Systems (since 2021), and organizer of IEEE conferences and special sessions. He was also Elected Member of the IEEE Signal Processing Theory and Methods Technical Committee and IEEE Biomedical Signal Processing Technical Committee (both in 2018-2023), and is currently Elected Member of the IEEE Machine Learning for Signal Processing Technical Committee and the IEEE Emerging Transportation Technology Testing (ET3) Technical Committee. His research team has won several best paper awards at international conferences and journals. His research on intelligent transportation systems has been featured by the BBC, Straits Times, Lianhe Zaobao, Channel 5, and numerous technology websites. Besides his academic efforts, the team of Dr. Justin Dauwels also collaborates intensely with local start-ups, SMEs, and agencies, in addition to MNCs, in the field of data-driven transportation, logistics, and medical data analytics. His academic lab has spawned four startups across a range of industries, ranging from AI for healthcare to autonomous vehicles.

Keynote Speaker Ⅱ

Prof. Wenwu Wang

University of Surrey, UK

Speech Title: Generative AI for Text to Sound Generation

Abstract: Text-to-audio generation aims to produce an audio clip based on a text prompt which is a language description of the audio content to be generated. This can be used as sound synthesis tools for film making, game design, virtual reality/metaverse, digital media, and digital assistants for text understanding by the visually impaired. To achieve cross modal text to audio generation, it is essential to comprehend the audio events and scenes within an audio clip, as well as interpret the textual information presented in natural language. In addition, learning the mapping and alignment of these two streams of information is crucial. Exciting developments have recently emerged in the field of automated audio-text cross modal generation. In this talk, we will give an introduction of this field, including problem description, potential applications, datasets, open challenges, recent technical progresses, and possible future research directions. We will focus on the deep generative AI methods for text to audio generation. We will start with the conditional audio generation method which we published in MLSP 2021 and used as the baseline system in DCASE 2023. We then move on to the discussion of several algorithms that we have developed recently, including AudioLDM, AudioLDM2, Re-AudioLDM, and AudioSep, which are getting increasingly popular in the signal processing, machine learning, and audio engineering communities.

A Brief Introduction to Prof. Wenwu Wang: Wenwu Wang is a Professor in Signal Processing and Machine Learning, University of Surrey, UK. He is also an AI Fellow at the Surrey Institute for People Centred Artificial Intelligence. His current research interests include signal processing, machine learning and perception, artificial intelligence, machine audition (listening), and statistical anomaly detection. He has (co)-authored over 300 papers in these areas. He has been recognized as a (co-)author or (co)-recipient of more than 15 accolades, including the 2022 IEEE Signal Processing Society Young Author Best Paper Award, ICAUS 2021 Best Paper Award, DCASE 2020 and 2023 Judge’s Award, DCASE 2019 and 2020 Reproducible System Award, and LVA/ICA 2018 Best Student Paper Award. He is an Associate Editor (2020-2025) for IEEE/ACM Transactions on Audio Speech and Language Processing and an Associate Editor (2014-2016) for IEEE Transactions on Multimedia. He was a Senior Area Editor (2019-2023) and Associate Editor (2014-2018) for IEEE Transactions on Signal Processing. He is the elected Chair (2023-2024) of IEEE Signal Processing Society (SPS) Machine Learning for Signal Processing Technical Committee, a Board Member (2023-2024) of IEEE SPS Technical Directions Board, the Vice Chair (2022-2024) of the EURASIP Technical Area Committee on Acoustic Speech and Music Signal Processing, an elected Member (2021-2026) of the IEEE SPS Signal Processing Theory and Methods Technical Committee. He was a Satellite Workshop Co-Chair for INTERSPEECH 2022, a Publication Co-Chair for IEEE ICASSP 2019, Local Arrangement Co-Chair of IEEE MLSP 2013, and Publicity Co-Chair of IEEE SSP 2009. He is a Satellite Workshop Co-Chair for IEEE ICASSP 2024, Special Session Co-Chair of IEEE MLSP 2024, and Technical Program Co-Chair of IEEE MLSP 2025. He has been a keynote or plenary speaker on more than 20 international conferences and workshops.