Bio

My name is Greg, I am a Research Engineer and a music technologist. My work typically orbits around Digital Signal Processing and Artificial Intelligence. Over the years, I’ve studied and built systems to help machines understand the musical signal the way humans do, and along the way, developed a soft spot for media hacking, data-driven riddles and jazz drumming.

I have been working at Gracenote since 2013, where I conduct research in Audio DSP and Deep Learning to solve problems in the field of Music Information retrieval (MIR). Prior to that, I joined Sennheiser as a contractor to build a Machine Learning framework for blind (sound) source localization.

In 2013, I completed the Masters program at the Georgia Tech Center for Music Technology (GTCMT) where I researched new ways of modeling musical creativity. Within the Music Intelligence Group led by Dr. Parag Chordia, I focused on artificial systems for rhythmic improvisation by researching and building innovative means of interaction between performers, listeners and machines. I joined the GTCMT to fuel my appetite for music, computing, and artificial intelligence, and to contribute to the development of interactive media in music making and music listening. My interests revolve around music information retrieval, digital signal processing, machine learning, interactive audio technologies, sound design, electro-acoustic engineering and sound perception.

I was born in Paris, grew up in Lyon, and moved from France to the United States when I was seventeen. A few years later I graduated from California State University Northridge with a Bachelor’s degree in Physics and Mathematics. During my REU at University of Illinois Urbana-Champaign, I investigated the modal vibrations of a fiberskyn frame-drum membrane using near-field acoustic holography techniques with Dr Steven Errede. During this program I developed a keen interest in sound technologies and in how engineering can be incorporated into the musical realm.