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Who should attend
- Software Developers and Engineers who need to develop audio visual data sensing systems.
- Data scientists who need to analyse audio and visual data.
- Product managers who need to initiate and manage projects and products related with audio-visual sensing.
- System architectures who need to integrate audio and visual data analytics modules into their solutions and systems.
- Project managers and consultants who need audio and visual data sense making skills to design, manage and evaluate such systems applications.
About the course
Real time audio-visual sensing systems make use of audio analytics and video analytics technology to translate audio-visual data into meaningful information in real time to aid business and operation. Demand for intelligent audio/video surveillance in real time is further accelerated due to additional features such as improved accuracy, enhanced reliability, and cost-effectiveness, along with its growing significance in business intelligence.
For example, they provide the means for the robot to interact intelligently with the outside world through acoustic and visual perception, which senses and enables robots to perform very flexible tasks such as moving around autonomously in a factory floor or outdoors. Their applications are plentiful and very challenging, such as human activity interpretations, human-computer interaction, real-time crowd surveillance in public place, human motion monitoring in healthcare, customer emotion recognition in retail.
This 4-day course presents the core technologies of audio-visual sensing and sense making fundamentals, and practical audio/video analytics skills and strategies for real-world industrial implementations through workshop sessions. The course will cover the how to process, integrate, interpret and act upon both audio and visual data in real time, such as fundamentals of audio processing in spatial and frequency domain, audio analytics and video analytics, motion and tracking, video classification and event recognition, as well as practical workshop sessions that allow participants to analyse, articulate and apply the new skills in their work and that of their teams and organisations.
This course is part of the Artificial Intelligence and Graduate Certificate in Intelligent Sensing Systems Series offered by NUS-ISS.
Upon completion of the course, students will be able to:
- Identify needs of real time audio-visual sensing technology and sense making methods in various industrial applications
- Design and apply audio-visual sense making methods to industrial use cases, including audio sensing, video analytics approaches.
- Assess the performance and usefulness of various real time audio-visual sense making technologies
- Apply critical analysis, evaluation and synthesis to a wide range of audio-visual sensing problems
- Analyse, articulate and apply the new skills in their work and that of their teams and organisations
What Will Be Covered
- Introduction to real time audio-visual sensing systems
- Real time video sensing: Modelling and processing
- Workshop on real time video processing, such as real time object tracking in video
- Real time video analytics: Motion and tracking
- Workshop on real time video analytics, such as human action video classification
- Real time audio analytics and recognition
- Workshop on audio analytics and recognition
- Sense making from multi-modal audio-visual data
- Workshop and case studies of real-time audio-visual sensing applications
- Written assessment test
Tian Jing currently lectures in the Analytics and Intelligent Systems Practice in the areas of artificial intelligence, data analytics, and machine learning. He received his Ph.D. degree from School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Prior to j...
Jen Hong develops algorithms. He specializes in deep learning, image processing and medical image diagnosis. He designs illustrations, web page and posters. He plays piano. He invented a mathematical model to analyze dry eye. He used deep learning to correct medical images. He trained deep learni...
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