Freeman, Jason

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    Data-to-music API: Real-time data-agnostic sonification with musical structure models
    (Georgia Institute of Technology, 2015-07) Tsuchiya, Takahiko ; Freeman, Jason ; Lerner, Lee W.
    In sonification methodologies that aim to represent the underlying data accurately, musical or artistic approaches are often dismissed as being not transparent, likely to distort the data, not generalizable, or not reusable for different data types. Scientific applications for sonification have been, therefore, hesitant to use approaches guided by artistic aesthetics and musical expressivity. All sonifications, however, may have musical effects on listeners, as our trained ears with daily exposure to music tend to naturally distinguish musical and non-musical sound relationships, such as harmony, rhythmic stability, or timbral balance. This study proposes to take advantage of the musical effects of sonification in a systematic manner. Data may be mapped to high-level musical parameters rather than to one-to-one low-level audio parameters. An approach to create models that encapsulate modulatable musical structures is proposed in the context of the new DataTo- Music JavaScript API. The API provides an environment for rapid development of data-agnostic sonification applications in a web browser, with a model-based modular musical structure system. The proposed model system is compared to existing sonification frameworks as well as music theory and composition models. Also, issues regarding the distortion of original data, transparency, and reusability of musical models are discussed.