Playing with Cases: Rendering Expressive Music with Case-Based Reasoning
Publication Type:
Journal ArticleSource:
AI Magazine, Volume 33, Issue 4, p.22-31 (2012)Keywords:
Computer Music; Computational CreativityAbstract:
This paper surveys long-term research on the problem of rendering expressive music by means of AI techniques with an emphasis on Case-Based Reasoning. Following a brief overview discussing why people prefer listening to expressive music instead of non-expressive synthesized music, we examine a representative selection of well-known approaches to expressive computer music performance with an emphasis on AI-related approaches. In the main part of the paper we focus on the existing CBR approaches to the problem of synthesizing expressive music, and particularly on TempoExpress, a case-based reasoning system developed at our Institute, for applying musically acceptable tempo transformations to monophonic audio recordings of musical performances. Finally we briefly describe an ongoing extension of our previous work consisting of complementing audio information with information about the gestures of the musician. Music is played through our bodies, therefore capturing the gesture of the performer is a fundamental aspect that has to be taken into account in future expressive music renderings. This paper is based on the “2011 Robert S. Engelmore Memorial Lecture” given by the first author at AAAI/IAAI 2011.
Reinforcement Learning with Case-Based Heuristics for RoboCup Soccer Keepaway
Publication Type:
Conference PaperSource:
2012 Brazilian Robotics Symposium and Latin American Robotics Symposium, Fortaleza, Brasil, p.7-13 (2012)Keywords:
Transfer learning; Robot soccerAutomated Generation of Cross-Domain Analogies via Evolutionary Computation
