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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

iZotope research team

Alexey Lukin

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.

iZotope research team

Shahan Nercessian

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.

iZotope research team

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).

iZotope research team

Andy Sarroff

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).

iZotope research team

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.

Recent Conferences & Sponsorships

Research Highlights

Carve out unwanted noise

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

Work in Daw or App

Zero-shot Singing Voice Conversion

 

In this paper, we propose the use of speaker embedding networks to perform zero-shot singing voice conversion, and suggest two architectures for its realization.

Shahan Nercessian
 

RX 8 music rebalance

Moog Ladder Filter Generalizations Based on State Variable Filters

In this paper, we propose a new style of continuous-time filter design composed of a cascade of 2nd-order state variable filters (SVFs) and a global feedback path.

Kurt James Werner and Russell McClellan
 

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.

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