AES Section Meeting Reports

Chicago - August 26, 2020

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Noise Reduction technology kick-started Dolby Laboratories 55 years ago, and in 2020, continues to be a focus, albeit in completely new contexts and applications. Despite advances in capture technology and the shift in recording mediums, the proliferation of content creators and capture devices has seen an ever increasing need for solutions to reduce noise in audio.

In this presentation, Nick Engel will dive into noise reduction techniques — from analog processing through to today's machine learning approaches. He'll discuss his own experiences as an engineer working on the digital implementation of Dolby B Noise Reduction, through to more recent experiences for helping to reduce noise in audio recordings at scale with This talk will explore how the types of noise and audio capture and recording methods have progressed, as well as approaches for reducing noise, from analog, to digital signal processing and through to the state of the art of deep learning techniques.

Nick Engel is the Senior Director, Media API R&D for the Media Processing platform. Nick is a graduate of the Australian National University where he studied Engineering (Interdisciplinary Systems) and Information Technology (Software Engineering). Nick grew up playing guitar and his passion for music and technology led him to join Lake Technology in 2003 as an Audio Signal Processing engineer developing audio enhancement technology for consumer devices. Nick joined Dolby Laboratories in 2004 and has held various positions in the Advanced Technology Group and Consumer Entertainment Group leading engineering teams in Sydney, San Francisco, Beijing, Shanghai and Shenzhen developing technology to bring immersive and enhanced audio experiences to a wide range of consumer devices in the home and on the go. Nick holds several patents and played a key role in launching new initiatives and developing many of Dolby's core audio technologies. Nick recently relocated to San Francisco to lead the R&D team for - a developer API platform for enabling media processing and analysis at scale.

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AES - Audio Engineering Society