Title:
Automatic accompaniment of vocal melodies in the context of popular music

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dc.contributor.advisor Chordia, Parag
dc.contributor.author Cao, Xiang en_US
dc.contributor.committeeMember Freeman, Jason
dc.contributor.committeeMember Weinberg, Gil
dc.contributor.department Music en_US
dc.date.accessioned 2009-06-08T19:02:24Z
dc.date.available 2009-06-08T19:02:24Z
dc.date.issued 2009-04-08 en_US
dc.description.abstract A piece of popular music is usually defined as a combination of vocal melody and instrumental accompaniment. People often start with the melody part when they are trying to compose or reproduce a piece of popular music. However, creating appropriate instrumental accompaniment part for a melody line can be a difficult task for non-musicians. Automation of accompaniment generation for vocal melodies thus can be very useful for those who are interested in singing for fun. Therefore, a computer software system which is capable of generating harmonic accompaniment for a given vocal melody input has been presented in this thesis. This automatic accompaniment system uses a Hidden Markov Model to assign chord to a given part of melody based on the knowledge learnt from a bank of vocal tracks of popular music. Comparing with other similar systems, our system features a high resolution key estimation algorithm which is helpful to adjust the generated accompaniment to the input vocal. Moreover, we designed a structure analysis subsystem to extract the repetition and structure boundaries from the melody. These boundaries are passed to the chord assignment and style player subsystems in order to generate more dynamic and organized accompaniment. Finally, prototype applications are discussed and the entire system is evaluated. en_US
dc.description.degree M.S. en_US
dc.identifier.uri http://hdl.handle.net/1853/28136
dc.publisher Georgia Institute of Technology en_US
dc.subject Pitch tracking en_US
dc.subject Key estimation en_US
dc.subject Structure analysis en_US
dc.subject Chord assignment en_US
dc.subject.lcsh Hidden Markov models
dc.subject.lcsh Music Data processing
dc.subject.lcsh Musical pitch
dc.subject.lcsh Popular music
dc.title Automatic accompaniment of vocal melodies in the context of popular music en_US
dc.type Text
dc.type.genre Thesis
dspace.entity.type Publication
local.contributor.corporatename College of Design
local.contributor.corporatename School of Music
local.relation.ispartofseries Master of Science in Music Technology
relation.isOrgUnitOfPublication c997b6a0-7e87-4a6f-b6fc-932d776ba8d0
relation.isOrgUnitOfPublication 92d2daaa-80f2-4d99-b464-ab7c1125fc55
relation.isSeriesOfPublication bb52c603-2646-4dfa-a9b7-9f81b43c419a
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