BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Chicago BEGIN:DAYLIGHT DTSTART:20240310T030000 TZOFFSETFROM:-0600 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:CDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20231105T010000 TZOFFSETFROM:-0500 TZOFFSETTO:-0600 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:CST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20240308T090402Z UID:9C32CDD6-2344-46DC-8C07-B31B0723BF5D DTSTART;TZID=America/Chicago:20240306T093000 DTEND;TZID=America/Chicago:20240306T110000 DESCRIPTION:Title: Signal Processing and Deep Learning for Practical Active Noise Control\n\nDigital active noise control (ANC) systems have gained s ignificant popularity (in ANC headphones and road noise cancellation in au tomobiles) in recent years\, thanks to the advancements in digital electro nics such as embedded processors and ADC/DAC converters. This progress has also led to the development of more complex\, multi-channels\, distribute d and efficient control algorithms for ANC systems. These algorithms allow for improved performance and better control of noise in a wide range of a pplications. The ease of implementation and lower costs associated with th ese systems have made them a popular choice for solving noise problems in various fields.\n\nIn this presentation\, I will discuss the practical asp ects of designing an ANC system for headphones and windows. I will showcas e some of the proof-of-concept systems developed at the Digital Signal Pro cessing Lab at Nanyang Technological University\, Singapore\, and demonstr ate how machine learning and digital signal processing techniques can be u tilized to detect noise patterns and control noise effectively.\n\nI will also provide an overview of the current advancements in ANC algorithms\, i ncluding multi-channel ANC\, virtual sensing and control\, selective noise control\, psychoacoustic ANC\, and other new research directions in ANC s ystems. 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.\n\nCo-sponsored by: Starkey\n\nSpeaker(s): Prof. Woon- Seng Gan\n\nAgenda: \n9:30 – 10:00 a.m. Meet and Greet (Coffee session)\ n\n10:00 – 10:05 a.m. Welcome Remarks by Dr. Martin McKinney\n\n10:05 – 11:00 a.m. Signal Processing and Deep Learning for Practical Active N oise Control by Prof. Woon-Seng Gan\, chair by Dr. Wenyu Jin\n\nBldg: Will iam F. Austin Education Center\, 6425 Washington Ave S\, Eden Prairie\, Mi nnesota\, United States\, 55344 LOCATION:Bldg: William F. Austin Education Center\, 6425 Washington Ave S\, Eden Prairie\, Minnesota\, United States\, 55344 ORGANIZER:wenyu.jin@ieee.org SEQUENCE:41 SUMMARY:IEEE SPS DISTINGUISHED LECTURER PROGRAM TWIN CITIES SP/COM CHAPTER SEMINAR 03/06/2024 URL;VALUE=URI:https://events.vtools.ieee.org/m/404973 X-ALT-DESC:Description: <br /><p>Title: <span style="text-decoration: under line\;"><strong>Signal Processing and Deep Learning for Practical Active N oise Control</strong></span></p>\n<p class="zfr3Q CDt4Ke " dir="ltr"><span class="C9DxTc ">Digital active noise control (ANC) systems have gained si gnificant popularity (in ANC headphones and road noise cancellation in aut omobiles) in recent years\, thanks to the advancements in digital electron ics such as embedded processors and ADC/DAC converters. </span><span class ="C9DxTc ">This progress has also led to the development of more complex\, multi-channels\, distributed and efficient control algorithms for ANC sys tems. These algorithms allow for improved performance and better control o f noise in a wide range of applications. The ease of implementation and lo wer costs associated with these systems have made them a popular choice fo r solving noise problems in various fields.</span></p>\n<p class="zfr3Q CD t4Ke " dir="ltr"><span class="C9DxTc ">In this presentation\, I will discu ss the practical aspects of designing an ANC system for headphones and win dows. I will showcase some of the proof-of-concept systems developed at th e Digital Signal Processing Lab at Nanyang Technological University\, Sing apore\, and demonstrate how machine learning and digital signal processing techniques can be utilized to detect noise patterns and control noise eff ectively.</span></p>\n<p class="zfr3Q CDt4Ke " dir="ltr"><span class="C9Dx Tc ">I will also provide an overview of the current advancements in ANC al gorithms\, including multi-channel ANC\, virtual sensing and control\, sel ective noise control\, psychoacoustic ANC\, and other new research directi ons 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.</span></p><br /><br />Agenda: <br /><p>9:3 0 &ndash\; 10:00 a.m.&nbsp\; &nbsp\; &nbsp\; &nbsp\; &nbsp\; <strong>Meet and Greet </strong>(Coffee session)</p>\n<p>10:00 &ndash\; 10:05 a.m. &nbs p\; &nbsp\; &nbsp\; &nbsp\;<strong>Welcome Remarks</strong>&nbsp\;by&nbsp\ ;<em>Dr. Martin McKinney</em></p>\n<p>10:05 &ndash\; 11:00 a.m. &nbsp\; &n bsp\; &nbsp\; &nbsp\;<strong> <span style="text-decoration: underline\;">S ignal Processing and Deep Learning for Practical Active Noise Control </sp an></strong>by Prof. Woon-Seng Gan\, chair by Dr. Wenyu Jin</p> END:VEVENT END:VCALENDAR