Matt Smith, Simon Holland<\/p>\n\n\n\n
Smith\u2019s constraint-based learning tool MOTIVE for exploring melody works within a constraint-based methodology. The aim of MOTIVE is to support beginners to explore the composition of melody. The work achieves potentially very general applicability to melody, irrespective of genre, by virtue of being based on the most fundamental psychologically grounded theory of melody currently available Narmour\u2019s (1989) cognitive theory of melody.<\/p>\n\n\n\n
Narmour\u2019s theory has known problems and limitations (Cumming, 1992), but has little competition as a theory of melody framed substantially in psychological terms. Narmour\u2019s (1989) analytical theory of tonal melody uses simple extensions to low level gestalt processing theory for melodic notes to predict how a listener will break a melody up into groups of contiguous notes, and which notes will be perceived as more important than others (other things being equal). This gives rise to hierarchical parse trees which recursively reduce the melody to simpler versions, roughly analogous to Lerdahl and Jackendoff (1983) TSR trees.<\/p>\n\n\n\n
A central contribution of Smith\u2019s work is that, in order to be able to make use of Narmour\u2019s theory computationally, an explicit, consistent computational model of the theory had to be refined and implemented. Having done this, Smith was able to test Narmour\u2019s published hand-produced analyses for consistency against the computational version. The tests showed the theory to be internally coherent, with some gaps to be filled in, but with no fatal internal flaws. This computational model then became the central component of Smith\u2019s teaching system, MOTIVE. MOTIVE uses a constraint based planner to parse melodies. MOTIVE is able to \u2018replan\u2019 or recompose melodies by navigating trees of related melodies, while holding constant, or varying structural features of the melody at any level. Thus an \u2018analysis by recomposition\u2019 strategy can be applied, as well as other teaching strategies.<\/p>\n\n\n\n
Apart from possible impacts in education, it is highly likely that Smith\u2019s work will serve as a useful computational tool to help explore, modify, or refine Narmour\u2019s theory.<\/p>\n\n\n\n
M. Smith & S. Holland, (1994) \u201cMOTIVE: A constraint-based tool for melody analysis and generation\u201d, In M. Smith, A. Smaill & G. Wiggins (eds), \u201cMusic Education: An Artificial Intelligence Approach\u201d, Springer-Verlag<\/p>\n\n\n\n
M. Smith & S. Holland, (1993) \u201cMOTIVE: A constraint-based tool for melody analysis and generation\u201d, Proceedings of Workshop on Artificial Intelligence, Music and Education, part of AIED-93, Edinburgh, Scotland<\/p>\n\n\n\n
M. Smith & S. Holland, (1992) \u201cAn AI tool for the Analysis and Generation of Melodies\u201d, Proceedings of International Computer Music Conference, San Jose, California, USA<\/p>\n\n\n\n
M. Smith & S. Holland, (1992) \u201cAn Intelligent Tutor for Melody Composition\u201d, Proceedings of International Conference on Systems Research, Informatics and Cybernetics, Baden-Baden, Germany<\/p>\n","protected":false},"excerpt":{"rendered":"
Matt Smith, Simon Holland Project Description Smith\u2019s constraint-based learning tool MOTIVE for exploring melody works within a constraint-based methodology. The aim of MOTIVE is to support beginners to explore the composition of melody. The work achieves potentially very general applicability to melody, irrespective of genre, by virtue of being based on the most fundamental psychologically […]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_unity_review_frequency":"","_unity_review_next_due":0,"_unity_review_reset_requested":false,"_unity_news_pull_enabled":false,"_unity_news_feed_url":"","_unity_news_items_per_page":10,"_unity_pubs_pull_enabled":false,"_unity_pubs_orocus":"","_unity_pubs_project_group":"","_unity_pubs_items_per_page":10,"_unity_research_event_page_slug":"","_unity_team_show_photos":true,"_unity_team_show_blurbs":true,"_unity_team_group_members":false,"_unity_team_profiles_per_row":3,"footnotes":""},"class_list":["post-2","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/research.stem.open.ac.uk\/artificial-intelligence-melody-and-education\/wp-json\/wp\/v2\/pages\/2","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/research.stem.open.ac.uk\/artificial-intelligence-melody-and-education\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/research.stem.open.ac.uk\/artificial-intelligence-melody-and-education\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/research.stem.open.ac.uk\/artificial-intelligence-melody-and-education\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/research.stem.open.ac.uk\/artificial-intelligence-melody-and-education\/wp-json\/wp\/v2\/comments?post=2"}],"version-history":[{"count":2,"href":"https:\/\/research.stem.open.ac.uk\/artificial-intelligence-melody-and-education\/wp-json\/wp\/v2\/pages\/2\/revisions"}],"predecessor-version":[{"id":5,"href":"https:\/\/research.stem.open.ac.uk\/artificial-intelligence-melody-and-education\/wp-json\/wp\/v2\/pages\/2\/revisions\/5"}],"wp:attachment":[{"href":"https:\/\/research.stem.open.ac.uk\/artificial-intelligence-melody-and-education\/wp-json\/wp\/v2\/media?parent=2"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}