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March 6 @ 10:30 am - 12:00 pm EST

Title: Signal Processing and Deep Learning for Practical Active Noise Control

Digital active noise control (ANC) systems have gained significant popularity (in ANC headphones and road noise cancellation in automobiles) in recent years, thanks to the advancements in digital electronics such as embedded processors and ADC/DAC converters. This progress has also led to the development of more complex, multi-channels, distributed and efficient control algorithms for ANC systems. These algorithms allow for improved performance and better control of noise in a wide range of applications. The ease of implementation and lower costs associated with these systems have made them a popular choice for solving noise problems in various fields.

In this presentation, I will discuss the practical aspects of designing an ANC system for headphones and windows. I will showcase some of the proof-of-concept systems developed at the Digital Signal Processing Lab at Nanyang Technological University, Singapore, and demonstrate how machine learning and digital signal processing techniques can be utilized to detect noise patterns and control noise effectively.

I will also provide an overview of the current advancements in ANC algorithms, including multi-channel ANC, virtual sensing and control, selective noise control, psychoacoustic ANC, and other new research directions in ANC systems. By providing insights into the latest developments in the field, I will give attendees a deeper understanding of the practical applications of ANC systems and the potential they hold for solving noise problems in various industries.

Co-sponsored by: Starkey

Speaker(s): Prof. Woon-Seng Gan

9:30 – 10:00 a.m. Meet and Greet (Coffee session)

10:00 – 10:05 a.m. Welcome Remarks by Dr. Martin McKinney

10:05 – 11:00 a.m. Signal Processing and Deep Learning for Practical Active Noise Control by Prof. Woon-Seng Gan, chair by Dr. Wenyu Jin

Bldg: William F. Austin Education Center, 6425 Washington Ave S, Eden Prairie, Minnesota, United States, 55344