TitleCombining CBR and fuzzy techniques to generate expressive music by imitation of human players
Publication TypeConference Paper
Year of Publication2005
Authorsde Mántaras RLópez
Conference NameBISCSE 2005: Forging new frontiers 40th of fuzzy pioneers (1965-2005) BISC Special event in honor of Prof. Lotfi A. Zadeh
PublisherUniversity of California
Pagination162 - 163 (extended abstract)
Abstract

One of the major difficulties in the automatic generation of music is to endow the resulting piece with the expressiveness that characterizes human performers. Following musical rules, no mater how sophisticated and complete they are, is not enough to achieve expression, and indeed computer music usually sounds monotonous and mechanical. The main problem is to grasp the performers personal touch, that is, the knowledge brought about when performing a score. A large part of this knowledge is implicit and very difficult to verbalize. For this reason, AI approaches based on declarative knowledge representations are very useful to model musical knowledge an indeed we represent such knowledge declaratively in our system, however they have serious limitations in grasping performance knowledge. An alternative approach, much closer to the observation – imitation - experimentation process observed in human performers, is that of directly using the performance knowledge implicit in examples of human performers and let the system imitate these performances. To achieve this, we have developped the SaxEx , a case-based reasoning system capable of generating expressive performances of melodies based on examples of human performances. CBR is indeed an appropriate methodology to solve problems by means of examples of already solved similar problems.