Loading…

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...

Full description

Saved in:
Bibliographic Details
Published in:Music theory online 2018-06, Vol.24 (2)
Main Author: White, Christopher Wm
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c298t-6dc1fc21ebd4da8daab5e7134077db84d7b291737d187feca7fcb86a36ff78513
cites cdi_FETCH-LOGICAL-c298t-6dc1fc21ebd4da8daab5e7134077db84d7b291737d187feca7fcb86a36ff78513
container_end_page
container_issue 2
container_start_page
container_title Music theory online
container_volume 24
creator White, Christopher Wm
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
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2082027644</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2082027644</sourcerecordid><originalsourceid>FETCH-LOGICAL-c298t-6dc1fc21ebd4da8daab5e7134077db84d7b291737d187feca7fcb86a36ff78513</originalsourceid><addsrcrecordid>eNpNkE1LAzEYhIMoWKsn_0DAo-z65mP3zR6lWBVbvOg55BNat01Nukj_vav14Glm4GEGhpBrBrWARjR3m32quax5LU_IhEGLlQAJp__8ObkoZQ3AmejUhNTzELw17oOarac_Iab8ZbKny-RDX2iKdDmUlTM9fQmHS3IWTV_C1Z9Oyfv84W32VC1eH59n94vK8U7tq9Y7Fh1nwXrpjfLG2CYgExIQvVXSo-UdQ4GeKYzBGYzOqtaINkZUDRNTcnPs3eX0OYSy1-s05O04qTkoDhxbKUfq9ki5nErJIepdXm1MPmgG-vcQPR6iudRcS_ENrUJSgQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2082027644</pqid></control><display><type>article</type><title>Feedback and Feedforward Models of Musical Key</title><source>Publicly Available Content Database</source><source>DOAJ Directory of Open Access Journals</source><creator>White, Christopher Wm</creator><creatorcontrib>White, Christopher Wm</creatorcontrib><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><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 &amp; 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>
fulltext fulltext
identifier ISSN: 1067-3040
ispartof Music theory online, 2018-06, Vol.24 (2)
issn 1067-3040
1067-3040
language eng
recordid cdi_proquest_journals_2082027644
source Publicly Available Content Database; DOAJ Directory of Open Access Journals
subjects Behavior
Harmonic analysis
Logic
Music theory
Pedagogy
title Feedback and Feedforward Models of Musical Key
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T11%3A09%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Feedback%20and%20Feedforward%20Models%20of%20Musical%20Key&rft.jtitle=Music%20theory%20online&rft.au=White,%20Christopher%20Wm&rft.date=2018-06&rft.volume=24&rft.issue=2&rft.issn=1067-3040&rft.eissn=1067-3040&rft_id=info:doi/10.30535/mto.24.2.4&rft_dat=%3Cproquest_cross%3E2082027644%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c298t-6dc1fc21ebd4da8daab5e7134077db84d7b291737d187feca7fcb86a36ff78513%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2082027644&rft_id=info:pmid/&rfr_iscdi=true