Arne is a composer of acoustic and electroacoustic music, and is an active software designer. His music has been performed throughout the world, and his research in intelligent music systems has been published and presented in international conferences. He teaches music and technology at SFU's School for the Contemporary Arts.
Chris is a graduate from the School of Contemporary Arts at Simon Fraser University. He is a composer and performer with a background in both traditional and electronic music. His interests include exploring new approaches to interactive musical performance and the use of generative systems within composition. Much of his work explores the use of music technology and its various functions within societies and cultures.
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Philippe is a professor in the School of Interactive Arts and Technology of Simon Fraser University. In his artistic practice, focused primarily on sonic arts, he is interested in studying and exploiting the various relationships and synergies between art, science and technology. He has been acting as a performer, director, composer, musician, producer and educator in many different contexts.
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GERP generates stylistically valid Electronic Dance Music using human informed machine-learning.
GERP is an attempt to generate stylistically valid EDM using human-informed machine-learning. We have employed experts (mainly Chris Anderson) to hand-transcribe 100 tracks in four genres: Breaks, House, Dubstep, and Drum and Bass. Aspects of transcription include musical details (drum beats, percussion parts, bass lines, melodic parts), timbral descriptions (i.e. “low synth kick, mid acoustic snare, tight noise closed hihat”), signal processing (i.e. the use of delay, reverb, compression and its alteration over time), and descriptions of overall musical form. This information is then compiled in a database, and machine analysed to produce data for generative purposes.
Two different systems have been created to interpret this data: GESMI (created by Arne Eigenfeldt/loadbang) and GEDMAS (created by Chris Anderson/Pittr Patter). GEDMAS began producing EDM tracks in June 2012, while GESMI produced her first fully autonomous generation in March 2013. It is interesting to note the similarities of the systems (due to the shared corpus) and the differences (due to the different creative choices made in the implementation).
Both systems are, for the moment, only using the Breaks corpus of 24 tracks.
Both systems will debut at MUME, in Sydney Australia, in June 2013. GEMSI will follow this a few weeks later at the aCoAx Festival in Milan, Italy.
Both systems are coded in MaxMSP and Max for Live, and use Ableton Live 9 for sequencing.
Chris Anderson's work in figuring out how to reference Live instruments through M4L has been integral to the success of automated timbral selections, as well as figuring out how to dump data into Live clips.
1) further exploration of autonomous timbral selection, using machine learning techniques on the corpus data, and the available Live instruments (fall 2013);
2) automated signal processing (late summer 2013);
3) complete analysis of the remaining corpus (House, Dubstep, Drum and Bass), and its inclusion in the generation algorithms.
Evolving Structures for Electronic Dance Music
Genetic and Evolutionary Computation Conference (GECCO). Amsterdam
A New Analytical Method For the Musical Study of Electronica
Proceedings of the Electroacoustic Music Studies Conference, Sforzando! New York,
Towards a Generative Electronica: A Progress Report
eContact! 14.4 Toronto Electroacoustic Symposium
Towards a Generative Electronica: Human Informed Machine Transcription and Analysis in MAXMSP
Proceedings of the Sound and Music Conference 2011, Padova Italy
The Human Fingerprint in Machine Generated Music
Proceedings of xCoAx2013: Computation, Communication, Aesthetics, and X Bergamo Italy
Considering Vertical and Horizontal Context in Corpus-based Generative Electronic Dance Music
The Fourth International Conference on Computational Creativity, Sydney (2013)
Generative Electronica Research Project