Digital Signal Processing Equipped with Machine Learning

Abstract: Advances in machine learning find rapid adoption in many fields ranging from communications, signal processing, and the automotive industry to healthcare, law, and forensics. In this talk, I briefly focus on a couple of research projects revolving around machine learning and signal processing: a) speech reconstruction for aphonic patients, b) forensic investigations through shoeprint analysis and vein pattern recognition, and c) NLP for schizophrenia detection. These research projects mainly rely on cutting-edge machine learning algorithms applied to speech and image processing areas, one for rehabilitation purposes for post-laryngectomised patients, the other for helping law enforcement agencies identify criminals/victims, and the last for diagnosing mental health issues.

Short Bio: Dr Hamid Sharifzadeh received his PhD from Nanyang Technological University (NTU) in Singapore in 2011. After completing his studies, he undertook two postdoctoral fellowships at NTU, focusing on speech and image processing, before joining Unitec Institute of Technology in Auckland, New Zealand in 2014 where he is currently an Associate Professor. His research interests lie in digital signal processing and machine learning. He has published over 50 papers in international journals and conferences, and he is a Senior Member of IEEE (SrMIEEE) and a Member of IET (MIET).