EarSketch: Teaching Computational Music Remixing in an Online Web Audio Based Learning Environment

            
@inproceedings{2015_3,
  abstract = {EarSketch is a novel approach to teaching computer science concepts via algorithmic music composition and remixing in the context of a digital audio workstation paradigm. This project includes a Python/Javascript coding environment, a digital audio workstation view, an audio loop browser, a social sharing site and an integrated curriculum. EarSketch is aimed at satisfying both artistic and pedagogical goals of introductory courses in computer music and computer science. This integrated platform has proven particularly effective at engaging culturally and economically diverse students in computing through music creation. EarSketch makes use of the Web Audio API as its primary audio engine for playback, effects processing and offline rendering of audio data. This paper explores the technical framework of EarSketch in greater detail and discusses the opportunities and challenges associated with using the Web Audio API to realize the project.},
  address = {Paris, France},
  author = {Mahadevan, Anand and Freeman, Jason and Magerko, Brian and Martinez, Juan Carlos},
  booktitle = {Proceedings of the International Web Audio Conference},
  editor = {Goldszmidt, Samuel and Schnell, Norbert and Saiz, Victor and Matuszewski, Benjamin},
  month = {January},
  pages = {},
  publisher = {IRCAM},
  series = {WAC '15},
  title = {EarSketch: Teaching Computational Music Remixing in an Online Web Audio Based Learning Environment},
  year = {2015},
  ISSN = {2663-5844}
}