Ashley Burgoyne
John Ashley Burgoyne is the Lecturer in Computational Musicology at the University of Amsterdam and a researcher in the Music Cognition Group at the Institute for Logic, Language, and Computation. Cross-appointed in Musicology and Artificial Intelligence, he is interested in understanding musical behaviour at the audio level, using large-scale experiments and audio corpora. His McGill–Billboard corpus of time-aligned chord and structure transcriptions has served as a backbone for audio chord estimation techniques. His ‘Hooked-on Music’ project reached hundreds of thousands of participants in almost every country on Earth while collecting data to understand long-term musical memory. Currently, he is working through the Amsterdam Music Lab to understand what people are hearing – and what they are ignoring – while they stream music every day.
Latest publications
Media attention
Maakt Joost Klein kans op Songfestivalwinst? ‘De top 10 moet lukken’Folia
En wéér staat Bohemian Rhapsody op 1, maar waarom eigenlijk?De Volkskrant
Kunnen we de ‘lijst der lijsten’ wetenschappelijk verklaren?De Volkskrant
De magie van 25x Top 2000NPO3
De Nacht van NTR over AI-muziek en het AI SongfestivalDe Nacht van
Kan AI de winnaar van het Songfestival voorspellen?NPO1
What makes music catchy?University of the Netherlands
Ashley Burgoyne on the science and mystery of listeningILLC Blog
Ashley Burgoyne en Janne Spijkervet over VPRO's AI SongfestivalNPO Radio 1
Ashley Burgoyne over kortere aandachtsspanne bij luisteraarsNOS.nl
Courses
Computational Musicology
In this course, we will learn to work specifically with audio data, the form of music most commonly consumed today, using the tools Spotify provides for working with its catalogue.
How Music Works I: Computational and Cognitive Perspectives
In the last two decades an important shift has occurred in music research, that is, from music as an art (or art object) to music as a process in which the performer, the listener, and music as sound play a central role. This transformation is most notable in the field of systematic musicology, which developed from “a mere extension of musicology” into a “complete reorientation of the discipline to fundamental questions which are non-historical in nature, [encompassing] research into the nature and properties of music as an acoustical, psychological and cognitive phenomenon” (Duckles & Pasler, 2001; Honing, 2006). These recent strands of music research will be interpreted in the context of the “cognitive revolution” in the humanities and the sciences. Next to an overview of the methods and techniques that became central to the contemporary musicologist’s toolkit, current developments will be discussed that explore what cognitive musicology can say about how music works.
Tweedejaarsproject BSc KI
In this course, students will work on an AI-related project in a small group of 3 to 5 students.
Quantitive Methods in Musicology
Quantitive Methods in Musicology
Onderzoeksproject Muziekwetenschap II
Perform independent and practice-based research in the fields of historical, cultural, and/or cognitive musicology.