Evolving Structures for Electronic Dance Music

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.