Chair: Yuki Mitsufuji (Sony)
received the BS and MS degrees in information science from Keio University in 2002 and 2004, respectively. He obtained the PhD degree in information science and technology from the University of Tokyo in 2020. Currently, he is leading Creative AI Lab at Sony Group Corporation while serving as Specially Appointed Associate Professor at Tokyo Institute of Technology. In 2021, his team organized Music Demixing (MDX) Challenge where Sony Music provided a professionally-produced music dataset for the evaluation of submitted systems to an online platform on AIcrowd.
Stefan Uhlich (Sony)
received the Dipl.-Ing. and PhD degree in electrical engineering from the University of Stuttgart, Germany, in 2006 and 2012, respectively. From 2007 to 2011 he was a research assistant at the Chair of System Theory and Signal Processing, University of Stuttgart. In this time he worked in the area of statistical signal processing, focusing especially on parameter estimation theory and methods. Since 2011, he is with the Sony Stuttgart Technology Center where he works as a Senior Principal Engineer on problems in music source separation and deep neural network compactization.
Giorgio Fabbro (Sony)
is an Engineer in Sony, working on music source separation and audio signal processing. He holds a M.Sc. in Informatics from the Technische Universität in München and is interested in the intersection of music technology and machine learning.
Fabian-Robert Stöter (Audioshake)
is the head of research at Audioshake where he is working on machine learning models for music processing. Before, he was a postdoctoral researcher at the Inria and University of Montpellier France. He received his PhD (Dr.-Ing.) at the International Audio Laboratories Erlangen in Germany. His research interests include supervised and unsupervised methods for music source separation and signal analysis of highly overlapped sources and sound source counting.
Igor Gadelha (Moises)
serves as the Head of Machine Learning at Moises, where he specializes in Music Information Retrieval. He obtained his Ph.D. in Computer Science from the Federal University of Rio Grande do Norte and completed an additional research stage at the Institute of Applied Sciences and Intelligent Systems (ISASI) under the auspices of the Consiglio Nazionale Delle Ricerche (CNR) in Italy. Igor's latest contributions include audio source separation research and overseeing multiple research labs dedicated to the development of new AI models in the realm of music practice and production.
Gordon Wichern (MERL)
is a Senior Principal Research Scientist at Mitsubishi Electric Research Laboratories. Gordon's research interests are at the intersection of signal processing and machine learning applied to speech, music, and environmental sounds. Prior to joining MERL, Gordon worked at iZotope inc. developing audio signal processing software, and at MIT Lincoln Laboratory where he worked in radar target tracking.