We present a virtual Electronic Dance Music (EDM) producer that generates stylistically accurate Breakbeat music. GESMI, the Generative Electronica Statistical Modeling Instrument, uses a corpus of human-analysed Breakbeat tracks dating from 1994--2010: the system learns relevant musical information in order to produce a wide variety of music within the genre using knowledge culled from the transcriptions.

We present GESMI (Generative Electronica Statistical Modeling Instrument), a software system that generates Electronic Dance Music (EDM) using evolutionary methods. While using machine learning, GESMI rests on a corpus analysed and transcribed by domain experts. We describe a method for generating the overall form of a piece and individual parts, including specific patterns sequences, using evolutionary algorithms. Lastly, we describe how the user can use contextually-relevant target features to query the generated database of strong individual patterns. As our main focus is upon artistic results, our methods themselves use an iterative, somewhat evolutionary, design process based upon our reaction to results.

In this paper, we describe a software system that generates unique musical compositions in realtime, created by four autonomous multi-agents. Given no explicit musical data, agents explore their environment, building beliefs through interactions with other agents via messaging and listening (to both audio and/or MIDI data), generating goals, and executing plans. The artistic focus of Coming Together is the actual process of convergence, heard during performance (each of which usually lasts about ten minutes): the movement from random individualism to converged ensemble interaction. If convergence is successful, four additional agents are instantiated that exploit the emergent harmony and rhythm through brief, but beautiful melodic gestures. Once these agents have completed their work, or if the original "explorer" agents fail to converge, the system resets itself, and the process begins again.

In this paper, we describe a software system that generates unique musical compositions in realtime, created by four autonomous multi-agents. Given no explicit musical data, agents explore their environment, building beliefs through interactions with other agents via messaging and listening (to both audio and/or MIDI data), generating goals, and executing plans. The artistic focus of Coming Together is the actual process of convergence, heard during performance (each of which usually lasts about ten minutes): the movement from random individualism to converged ensemble interaction. If convergence is successful, four additional agents are instantiated that exploit the emergent harmony and rhythm through brief, but beautiful melodic gestures. Once these agents have completed their work, or if the original "explorer" agents fail to converge, the system resets itself, and the process begins again.