GERP generates 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).
Eigenfeldt, A., Pasquier, P. “Evolving Structures in Electronic Dance Music”, Genetic and Evolutionary Computation Conference (GECCO), Amsterdam, 2013 .
Eigenfeldt, A. “Generating Electronica – A Virtual Producer and Virtual DJ”, ACM Creativity and Cognition, Sydney, 2013.
Eigenfeldt, A., Pasquier, P. “Considering Vertical and Horizontal Context in Corpus-based Generative Electronic Dance Music”, International Conference on Computational Creativity (ICCC), Sydney, 2013.
Eigenfeldt, A. “Towards a Generative Electronica: A Progress Report” eContact! 14.4 Toronto Electroacoustic Symposium 2011.
Eigenfeldt, A., Pasquier, P. “Towards a Generative Electronica: Human-Informed Machine Transcription and Analysis in MaxMSP”, Proceedings of Sound and Music Computing Conference, Padua, 2011.