Feedback Delay Networks for Artificial Reverberation

Abstract: Feedback delay networks (FDNs) are recursive filters widely used for artificial reverberation and decorrelation. While vast literature exists on a wide variety of reverb topologies, FDNs provide a unifying framework to design and analyze delay-based reverberators. This talk reviews recent advancements in the FDN theory, such as losslessness, modal and echo representations, and MIMO allpass properties and decorrelation. Many extensions to the FDN were proposed, including time-varying matrices, scattering matrices, high-order attenuation filters, directional reverberation, and coupled room reverberators.

Presentation Recording

Code: https://github.com/SebastianJiroSchlecht/fdnToolbox
Website: https://www.sebastianjiroschlecht.com/

Bio: Sebastian J. Schlecht is a Professor of Practice for Sound in Virtual Reality - a joint appointment at the Acoustics Labs, Department of Signal Processing and Acoustics and Media Labs, Department of Art and Media, of Aalto University, Finland. He received the Diploma in Applied Mathematics from the University of Trier, Germany in 2010 and an M.Sc. degree in Digital Music Processing from Queen Mary University of London, U.K., in 2011. In 2017, he received a Doctoral degree at the International Audio Laboratories Erlangen, Germany, on the topic of artificial spatial reverberation and reverberation enhancement systems. From 2012 to 2019, Dr. Schlecht was also external research and development consultant and lead developer of the 3D Reverb algorithm at the Fraunhofer IIS, Erlangen, Germany. Between 2010 and 2019, he spent half his working time as a touring and recording musician playing internationally with bands such as Mighty Oaks, David Lemaitre, and Get Well Soon.

His research interests are acoustic modeling and auditory perception of acoustics, analysis and synthesis of feedback systems, music information retrieval, and virtual and augmented reality and their artistic applications. He is the recipient of multiple journal and conference best paper awards, including JAES (2020), WASPAA (2019), DAFx (2018, 2021, 2022), and AES AVAR (2018).