@PhDThesis{        burgoyne2011-stochastic,
    author       = {Burgoyne, John Ashley},
    year         = {2011},
    title        = {Stochastic processes and database-driven Musicology},
    abstract     = {For more than a decade, music information science and
                   musicology have been at what Nicholas Cook has described
                   as a 'moment of opportunity' for collaboration on
                   database-driven musicology. The literature contains
                   relatively few examples of mathematical tools that are
                   suitable for analysing temporally structured data like
                   music, however, and there are surprisingly few large
                   databases of music that contain information at the
                   semantic levels of interest to musicologists. This
                   dissertation compiles a bibliography of the most important
                   concepts from probability and statistics for analysing
                   musical data, reviews how previous researchers have used
                   statistics to study temporal relationships in music, and
                   presents a new corpus of carefully curated chord labels
                   from more than 1000 popular songs from the latter half of
                   the twentieth century, as ranked by Billboard magazine's
                   Hot 100 chart. The corpus is based on a careful sampling
                   methodology that maintained cost efficiency while ensuring
                   that the corpus is well suited to drawing conclusions
                   about how harmonic practises may have evolved over time
                   and to what extent they may have affected songs'
                   popularity. This dissertation also introduces techniques
                   new to the musicological community for analysing databases
                   of this size and scope, most importantly the
                   Dirichlet-multinomial distribution and constraint-based
                   structure learning for causal Bayesian networks. The
                   analysis confirms some common intuitions about harmonic
                   practises in popular music and suggests several intriguing
                   directions for further research.},
    keywords     = {computational musicology},
    mendeley-tags= {computational musicology},
    school       = {McGill University},
    type         = {Ph.D. Thesis},
    url          = {https://escholarship.mcgill.ca/concern/theses/d217qt98k?locale=en}
}

{"_id":"hbEtq9EkWK48m67St","bibbaseid":"burgoyne-stochasticprocessesanddatabasedrivenmusicology-2011","authorIDs":[],"author_short":["Burgoyne, J. A."],"bibdata":{"bibtype":"phdthesis","type":"Ph.D. Thesis","author":[{"propositions":[],"lastnames":["Burgoyne"],"firstnames":["John","Ashley"],"suffixes":[]}],"year":"2011","title":"Stochastic processes and database-driven Musicology","abstract":"For more than a decade, music information science and musicology have been at what Nicholas Cook has described as a 'moment of opportunity' for collaboration on database-driven musicology. The literature contains relatively few examples of mathematical tools that are suitable for analysing temporally structured data like music, however, and there are surprisingly few large databases of music that contain information at the semantic levels of interest to musicologists. This dissertation compiles a bibliography of the most important concepts from probability and statistics for analysing musical data, reviews how previous researchers have used statistics to study temporal relationships in music, and presents a new corpus of carefully curated chord labels from more than 1000 popular songs from the latter half of the twentieth century, as ranked by Billboard magazine's Hot 100 chart. The corpus is based on a careful sampling methodology that maintained cost efficiency while ensuring that the corpus is well suited to drawing conclusions about how harmonic practises may have evolved over time and to what extent they may have affected songs' popularity. This dissertation also introduces techniques new to the musicological community for analysing databases of this size and scope, most importantly the Dirichlet-multinomial distribution and constraint-based structure learning for causal Bayesian networks. The analysis confirms some common intuitions about harmonic practises in popular music and suggests several intriguing directions for further research.","keywords":"computational musicology","mendeley-tags":"computational musicology","school":"McGill University","url":"https://escholarship.mcgill.ca/concern/theses/d217qt98k?locale=en","bibtex":"@PhDThesis{ burgoyne2011-stochastic,\n author = {Burgoyne, John Ashley},\n year = {2011},\n title = {Stochastic processes and database-driven Musicology},\n abstract = {For more than a decade, music information science and\n musicology have been at what Nicholas Cook has described\n as a 'moment of opportunity' for collaboration on\n database-driven musicology. The literature contains\n relatively few examples of mathematical tools that are\n suitable for analysing temporally structured data like\n music, however, and there are surprisingly few large\n databases of music that contain information at the\n semantic levels of interest to musicologists. This\n dissertation compiles a bibliography of the most important\n concepts from probability and statistics for analysing\n musical data, reviews how previous researchers have used\n statistics to study temporal relationships in music, and\n presents a new corpus of carefully curated chord labels\n from more than 1000 popular songs from the latter half of\n the twentieth century, as ranked by Billboard magazine's\n Hot 100 chart. The corpus is based on a careful sampling\n methodology that maintained cost efficiency while ensuring\n that the corpus is well suited to drawing conclusions\n about how harmonic practises may have evolved over time\n and to what extent they may have affected songs'\n popularity. This dissertation also introduces techniques\n new to the musicological community for analysing databases\n of this size and scope, most importantly the\n Dirichlet-multinomial distribution and constraint-based\n structure learning for causal Bayesian networks. The\n analysis confirms some common intuitions about harmonic\n practises in popular music and suggests several intriguing\n directions for further research.},\n keywords = {computational musicology},\n mendeley-tags= {computational musicology},\n school = {McGill University},\n type = {Ph.D. Thesis},\n url = {https://escholarship.mcgill.ca/concern/theses/d217qt98k?locale=en}\n}\n\n","author_short":["Burgoyne, J. A."],"key":"burgoyne2011-stochastic","id":"burgoyne2011-stochastic","bibbaseid":"burgoyne-stochasticprocessesanddatabasedrivenmusicology-2011","role":"author","urls":{"Paper":"https://escholarship.mcgill.ca/concern/theses/d217qt98k?locale=en"},"keyword":["computational musicology"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"phdthesis","biburl":"https://hmb.sampaio.me/bibliografia.bib.txt","creationDate":"2020-11-29T13:22:52.172Z","downloads":0,"keywords":["computational musicology"],"search_terms":["stochastic","processes","database","driven","musicology","burgoyne"],"title":"Stochastic processes and database-driven Musicology","year":2011,"dataSources":["n6MFY2CscQLDpJ7nT"]}