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ItemData-Driven Live Coding with DataToMusic API(Georgia Institute of Technology, 2016-04) Tsuchiya, Takahiko ; Freeman, Jason ; Lerner, Lee W. ; Georgia Institute of Technology. Center for Music Technology ; Georgia Tech Research InstituteCreating interactive audio applications for web browsers often involves challenges such as time synchronization between non-audio and audio events within thread constraints and format-dependent mapping of data to synthesis parameters. In this paper, we describe a unique approach for these issues with a data-driven symbolic music application programming interface (API) for rapid and interactive development. We introduce DataToMusic (DTM) API, a data-sonification tool set for web browsers that utilizes the Web Audio API1 as the primary means of audio rendering. The paper demonstrates the possibility of processing and sequencing audio events at the audio-sample level by combining various features of the Web Audio API, without relying on the ScriptProcessorNode, which is currently under a redesign. We implemented an audio event system in the clock and synthesizer classes in the DTM API, in addition to a modular audio effect structure and a exible data-to-parameter mapping interface. For complex real-time configuration and sequencing, we also present a model system for creating reusable functions with a data-agnostic interface and symbolic musical transformations. Using these tools, we aim to create a seamless connection between high-level (musical structure) and low-level (sample rate) processing in the context of real-time data sonification.
ItemDirected Evolution in Live Coding Music Performance(Georgia Institute of Technology, 2020-10-24) Dasari, Sandeep ; Freeman, Jason ; Georgia Institute of Technology. Center for Music TechnologyGenetic algorithms are extensively used to understand, simulate, and create works of art and music. In this paper, a similar approach is taken to apply basic evolutionary algorithms to perform music live using code. Often considered an improvisational or experimental performance, live coding music comes with its own set of challenges. Genetic algorithms offer potential to address these long-standing challenges. Traditional evolutionary applications in music focused on novelty search to create new sounds, sequences of notes or chords, and effects. In contrast, this paper focuses on live performance to create directed evolving musical pieces. The paper also details some key design decisions, implementation, and usage of a novel genetic algorithm API created for a popular live coding language.