Each seminar highlights a different speaker who will discuss their latest research projects, cutting-edge technology or what is happening within certain technological industries. These industries include biomedical technologies or microsystems, assistive technologies, automation and intelligent systems, unmanned systems, advanced manufacturing and composite materials.
Title:
2D Cartesian Sound Source Localization in an Indoor Reverberant Environment Using Deep Learning
Abstract:
Robots without proficient auditory functions often struggle in unpredictable situations and are largely ineffective in human collaboration and interaction. Sound perception often surpasses visual abilities, especially in dark or cluttered environments. This exceptional feature of auditory perception can be pivotal: aiding disaster robots in locating victims, assisting self-driving cars in avoiding obscured obstacles or pedestrians, and facilitating seamless human-robot interactions even through physical barriers. For accurate sound source pinpointing in real-world scenarios, it is essential to consider the complexity posed by sound wave propagation in such a complex environment. Tackling these issues, this study introduces an innovative method for two-dimensional (2D) sound source localization in Cartesian coordinate system in cluttered, real-world indoor settings. This novel method leverages the capabilities of both conventional method and deep learning method by utilizing sound signal combined with environment maps generated by robotic SLAM for the first time, drawing upon information from incoming sound signals and environmental geometry. Ultimately, this research novel method can predict the location of a sound source in 2D Cartesian coordinate with just using small amount of training data faster and easier and more precise due to using sound signal and geometry of the environment. This groundwork paves the way for subsequent studies, adapting the strategy for spaces with multiple sound sources and intricate indoor areas where humans and robots might cooperate in separate rooms.
Bio:
Dr. Maziar Abhari received his PhD from Wichita State University in 2024, his master from Tarbiat Modares University, Iran in 2017 and his bachelor from Babol Noshirvani University of Technology, Iran, in 2014 all in Mechanical Engineering. He has recently joined University of Texas at Arlington as an Adjunct Assistant Professor in Mechanical and Aerospace Engineering department. During his research in PhD, he developed a new method for localizing sound sources in an indoor environment. His new method involved not only the sound captured by microphone, but also the geometry of the environment to increase the accuracy of localization and make cartesian localization possible. During his research in master, he also developed a rehabilitation robot for assisting stroke patients during arm rehabilitation. His current research interests are in Robotics, Control, Machine Learning and Computer Vision field.