iZotope research team
We drive the algorithmic vision for iZotope products
Through scientific leadership and innovation in digital signal processing, iZotope's industry-leading research team has contributed meaningful developments to the industry at large, and the iZotope products you know and love. Learn more about the team, and opportunities to join, below.
Meet the team
Alexey is iZotope’s Principal DSP Engineer. He earned his M.S. (2003) and Ph.D. (2006) in computer science from Lomonosov Moscow State University, Russia. He specializes in audio signal processing, with particular interest in crossovers with image processing in spectral analysis, noise reduction, and multiresolution filter banks. Alexey’s published work includes over 30 papers and 8 patents. He has been acknowledged with an Academy of Motion Picture Arts and Sciences' Scientific and Engineering Award for the development of iZotope RX—the audio repair toolkit.
Dr. Shahan Nercessian is a Staff Research Engineer at iZotope, Inc. He received his B.S., M.S. and Ph.D. degrees in Electrical Engineering from Tufts University in 2007, 2009, and 2012, respectively. Prior to joining iZotope, he was a Member of Technical Staff at MIT Lincoln Laboratory. He joined iZotope in 2017, where he has developed new DSP and machine learning algorithms for products like RX, Neutron, Nectar, and VocalSynth. His research interests include speech restoration, neural audio synthesis, vocal processing, and automatic mixing. He is an avid jazz musician, and continues to produce and play his own genre-bending original music.
Kurt James Werner
Dr. Kurt James Werner is a Research Engineer at iZotope. Prior to this, he was Assistant Professor of Audio at the Sonic Arts Research Centre of Queen’s University Belfast. He earned a Ph.D. in Computer-Based Music Theory and Acoustics from Stanford University’s CCRMA and both a B.S. in General Engineering (secondary field: Acoustics) and B.Music in Theory and Composition from UIUC. His publications include over 40 papers, and was awarded best student paper by the IEEE WASPAA (2015) and second best paper by DAFx (2020). As co-author he was also awarded best paper (DAFx, 2018), best student paper (AES, 2017), best paper no. 3 (DAFx, 2016), and a best paper honorable mention (DAFx, 2015).
Andy Sarroff is a Research Engineer at iZotope. He received an MM in Music Technology from New York University and a PhD in Computer Science from Dartmouth College with a dissertation on “Complex Neural Networks for Audio.” He has been a visiting researcher in the Speech & Audio group at Mitsubishi Electric Research Laboratories (MERL), as well as Columbia University’s Laboratory for the Recognition and Organization of Speech and Audio (LabROSA). Andy has served on the board of the International Society for Music Information retrieval and is currently a Chair of the Audio Engineering Society’s Technical Committee on Machine Learning and Artificial Intelligence for Audio (TC-MLAI).
François G. Germain
Dr. Germain works as a Research Engineer at iZotope Inc. in Cambridge, MA. He received a Ph.D. in Computer-Based Music Theory and Acoustics and an M.Sc. in Electrical Engineering from Stanford University (Stanford, CA) respectively in 2019 and 2014, an M.A. in Music Technology from McGill University (Montreal, Canada) in 2011 and a Diplôme d’Ingénieur from Ecole Polytechnique (Palaiseau, France) in 2010. He was awarded the Best Student Paper Awards at the 2013 INTERSPEECH Conference and the 142nd AES Convention, and the Best Reviewer Awards at the 2020 DAFx and the 2020 ISMIR Conferences.
Speech Dereverberation using Recurrent Neural Networks
In this paper, we show how a simple reformulation allows us to adapt blind source separation techniques to the problem of speech dereverberation and, accordingly, train a bidirectional recurrent neural network (BRNN) for this task.
Shahan Nercessian and Alexey Lukin
Energy-Preserving Time-Varying Schroeder Allpass Filters and Multichannel Extensions
We propose time-varying Schroeder allpass filters and Gerzon allpass reverberators that remain energy preserving irrespective of arbitrary variation of their allpass gains or feed- back matrices over time.
Kurt James Werner, François G. Germain, and Cory S. Goldsmith
Blind Arbitrary Reverb Matching
We propose a model architecture for performing reverb matching and provide subjective experimental results suggesting that the reverb matching model can perform as well as a human.
Andy Sarroff and Roth Michaels
Audio Research Internship Program
Our Audio Research Interns help explore new technologies under the mentorship of a relevant research team-member. This is a 3–4 month program.