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Feedback and Feedforward Models of Musical Key
This study begins by drawing a distinction between two ways of framing the concept of musical key. Feedforward models understand key as arising from immediately apparent surface characteristics like the distribution of pitch classes or a melody’s intervallic content. Feedback models, on the other ha...
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Published in: | Music theory online 2018-06, Vol.24 (2) |
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description | This study begins by drawing a distinction between two ways of framing the concept of musical key.
Feedforward
models understand key as arising from immediately apparent surface characteristics like the distribution of pitch classes or a melody’s intervallic content.
Feedback
models, on the other hand, understand key as being determined in tandem with other domains. Here, key arises from the surface being organized into other more complicated musical groupings or schemata—harmonic progressions, cadences, prolongations, meter, etc.—that themselves are informed by the music’s tonal center. While much music theory and theory pedagogy have acknowledged that feedback occurs in various approaches to tonality, formal modeling in the fields of music cognition and computation has focused primarily on feedforward systems. This article attempts to right this imbalance by presenting a corpus-based feedback computational model that can be tested against human behavior. My model will identify a passage’s key by organizing a surface into its constituent harmonies. Here, harmonic organization and key will be integrated into a feedback system with the ideal key being that which produces the ideal harmonic analysis, and vice versa. To validate the resulting model, its behavior is compared to that of other published tonal models, to the behaviors of undergraduate music students, and to the intuitions of professional music theorists. |
doi_str_mv | 10.30535/mto.24.2.4 |
format | article |
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Feedforward
models understand key as arising from immediately apparent surface characteristics like the distribution of pitch classes or a melody’s intervallic content.
Feedback
models, on the other hand, understand key as being determined in tandem with other domains. Here, key arises from the surface being organized into other more complicated musical groupings or schemata—harmonic progressions, cadences, prolongations, meter, etc.—that themselves are informed by the music’s tonal center. While much music theory and theory pedagogy have acknowledged that feedback occurs in various approaches to tonality, formal modeling in the fields of music cognition and computation has focused primarily on feedforward systems. This article attempts to right this imbalance by presenting a corpus-based feedback computational model that can be tested against human behavior. My model will identify a passage’s key by organizing a surface into its constituent harmonies. Here, harmonic organization and key will be integrated into a feedback system with the ideal key being that which produces the ideal harmonic analysis, and vice versa. To validate the resulting model, its behavior is compared to that of other published tonal models, to the behaviors of undergraduate music students, and to the intuitions of professional music theorists.</description><identifier>ISSN: 1067-3040</identifier><identifier>EISSN: 1067-3040</identifier><identifier>DOI: 10.30535/mto.24.2.4</identifier><language>eng</language><publisher>Chicago: Society for Music Theory</publisher><subject>Behavior ; Harmonic analysis ; Logic ; Music theory ; Pedagogy</subject><ispartof>Music theory online, 2018-06, Vol.24 (2)</ispartof><rights>Jun 2018. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the associated terms available at http://www.mtosmt.org/about.html</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c298t-6dc1fc21ebd4da8daab5e7134077db84d7b291737d187feca7fcb86a36ff78513</citedby><cites>FETCH-LOGICAL-c298t-6dc1fc21ebd4da8daab5e7134077db84d7b291737d187feca7fcb86a36ff78513</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2082027644?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,860,25732,27903,27904,36991,44569</link.rule.ids></links><search><creatorcontrib>White, Christopher Wm</creatorcontrib><title>Feedback and Feedforward Models of Musical Key</title><title>Music theory online</title><description>This study begins by drawing a distinction between two ways of framing the concept of musical key.
Feedforward
models understand key as arising from immediately apparent surface characteristics like the distribution of pitch classes or a melody’s intervallic content.
Feedback
models, on the other hand, understand key as being determined in tandem with other domains. Here, key arises from the surface being organized into other more complicated musical groupings or schemata—harmonic progressions, cadences, prolongations, meter, etc.—that themselves are informed by the music’s tonal center. While much music theory and theory pedagogy have acknowledged that feedback occurs in various approaches to tonality, formal modeling in the fields of music cognition and computation has focused primarily on feedforward systems. This article attempts to right this imbalance by presenting a corpus-based feedback computational model that can be tested against human behavior. My model will identify a passage’s key by organizing a surface into its constituent harmonies. Here, harmonic organization and key will be integrated into a feedback system with the ideal key being that which produces the ideal harmonic analysis, and vice versa. To validate the resulting model, its behavior is compared to that of other published tonal models, to the behaviors of undergraduate music students, and to the intuitions of professional music theorists.</description><subject>Behavior</subject><subject>Harmonic analysis</subject><subject>Logic</subject><subject>Music theory</subject><subject>Pedagogy</subject><issn>1067-3040</issn><issn>1067-3040</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNpNkE1LAzEYhIMoWKsn_0DAo-z65mP3zR6lWBVbvOg55BNat01Nukj_vav14Glm4GEGhpBrBrWARjR3m32quax5LU_IhEGLlQAJp__8ObkoZQ3AmejUhNTzELw17oOarac_Iab8ZbKny-RDX2iKdDmUlTM9fQmHS3IWTV_C1Z9Oyfv84W32VC1eH59n94vK8U7tq9Y7Fh1nwXrpjfLG2CYgExIQvVXSo-UdQ4GeKYzBGYzOqtaINkZUDRNTcnPs3eX0OYSy1-s05O04qTkoDhxbKUfq9ki5nErJIepdXm1MPmgG-vcQPR6iudRcS_ENrUJSgQ</recordid><startdate>201806</startdate><enddate>201806</enddate><creator>White, Christopher Wm</creator><general>Society for Music Theory</general><scope>AAYXX</scope><scope>CITATION</scope><scope>A3D</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AVQMV</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DJMCT</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>201806</creationdate><title>Feedback and Feedforward Models of Musical Key</title><author>White, Christopher Wm</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c298t-6dc1fc21ebd4da8daab5e7134077db84d7b291737d187feca7fcb86a36ff78513</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Behavior</topic><topic>Harmonic analysis</topic><topic>Logic</topic><topic>Music theory</topic><topic>Pedagogy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>White, Christopher Wm</creatorcontrib><collection>CrossRef</collection><collection>Music Periodicals Database</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Arts Premium Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>Music & Performing Arts Collection</collection><collection>ProQuest Central Korea</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Music theory online</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>White, Christopher Wm</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Feedback and Feedforward Models of Musical Key</atitle><jtitle>Music theory online</jtitle><date>2018-06</date><risdate>2018</risdate><volume>24</volume><issue>2</issue><issn>1067-3040</issn><eissn>1067-3040</eissn><abstract>This study begins by drawing a distinction between two ways of framing the concept of musical key.
Feedforward
models understand key as arising from immediately apparent surface characteristics like the distribution of pitch classes or a melody’s intervallic content.
Feedback
models, on the other hand, understand key as being determined in tandem with other domains. Here, key arises from the surface being organized into other more complicated musical groupings or schemata—harmonic progressions, cadences, prolongations, meter, etc.—that themselves are informed by the music’s tonal center. While much music theory and theory pedagogy have acknowledged that feedback occurs in various approaches to tonality, formal modeling in the fields of music cognition and computation has focused primarily on feedforward systems. This article attempts to right this imbalance by presenting a corpus-based feedback computational model that can be tested against human behavior. My model will identify a passage’s key by organizing a surface into its constituent harmonies. Here, harmonic organization and key will be integrated into a feedback system with the ideal key being that which produces the ideal harmonic analysis, and vice versa. To validate the resulting model, its behavior is compared to that of other published tonal models, to the behaviors of undergraduate music students, and to the intuitions of professional music theorists.</abstract><cop>Chicago</cop><pub>Society for Music Theory</pub><doi>10.30535/mto.24.2.4</doi><oa>free_for_read</oa></addata></record> |
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subjects | Behavior Harmonic analysis Logic Music theory Pedagogy |
title | Feedback and Feedforward Models of Musical Key |
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