@Article{          sturm.ea2019-machine,
    author       = {Sturm, Bob L. and Ben-Tal, Oded and Monaghan, {\'{U}}na
                   and Collins, Nick and Herremans, Dorien and Chew, Elaine
                   and Hadjeres, Ga{\"{e}}tan and Deruty, Emmanuel and
                   Pachet, Fran{\c{c}}ois},
    year         = {2019},
    title        = {Machine learning research that matters for music
                   creation: A case study},
    abstract     = {Research applying machine learning to music modelling and
                   generation typically proposes model architectures,
                   training methods and datasets, and gauges system
                   performance using quantitative measures like sequence
                   likelihoods and/or qualitative listening tests. Rarely
                   does such work explicitly question and analyse its
                   usefulness for and impact on real-world practitioners, and
                   then build on those outcomes to inform the development and
                   application of machine learning. This article attempts to
                   do these things for machine learning applied to music
                   creation. Together with practitioners, we develop and use
                   several applications of machine learning for music
                   creation, and present a public concert of the results. We
                   reflect on the entire experience to arrive at several ways
                   of advancing these and similar applications of machine
                   learning to music creation.},
    doi          = {10.1080/09298215.2018.1515233},
    issn         = {0929-8215},
    journal      = {Journal of New Music Research},
    keywords     = {Applied machine learning,computational creativity,folk
                   music,music generation,music information retrieval},
    mendeley-tags= {music information retrieval},
    month        = {jan},
    number       = {1},
    pages        = {36--55},
    url          = {https://www.tandfonline.com/doi/full/10.1080/09298215.2018.1515233},
    volume       = {48}
}

{"_id":"CLeYZM8TM9d9QtsB9","bibbaseid":"sturm-bental-monaghan-collins-herremans-chew-hadjeres-deruty-etal-machinelearningresearchthatmattersformusiccreationacasestudy-2019","authorIDs":[],"author_short":["Sturm, B. L.","Ben-Tal, O.","Monaghan, Ú.","Collins, N.","Herremans, D.","Chew, E.","Hadjeres, G.","Deruty, E.","Pachet, F."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Sturm"],"firstnames":["Bob","L."],"suffixes":[]},{"propositions":[],"lastnames":["Ben-Tal"],"firstnames":["Oded"],"suffixes":[]},{"propositions":[],"lastnames":["Monaghan"],"firstnames":["Úna"],"suffixes":[]},{"propositions":[],"lastnames":["Collins"],"firstnames":["Nick"],"suffixes":[]},{"propositions":[],"lastnames":["Herremans"],"firstnames":["Dorien"],"suffixes":[]},{"propositions":[],"lastnames":["Chew"],"firstnames":["Elaine"],"suffixes":[]},{"propositions":[],"lastnames":["Hadjeres"],"firstnames":["Gaëtan"],"suffixes":[]},{"propositions":[],"lastnames":["Deruty"],"firstnames":["Emmanuel"],"suffixes":[]},{"propositions":[],"lastnames":["Pachet"],"firstnames":["François"],"suffixes":[]}],"year":"2019","title":"Machine learning research that matters for music creation: A case study","abstract":"Research applying machine learning to music modelling and generation typically proposes model architectures, training methods and datasets, and gauges system performance using quantitative measures like sequence likelihoods and/or qualitative listening tests. Rarely does such work explicitly question and analyse its usefulness for and impact on real-world practitioners, and then build on those outcomes to inform the development and application of machine learning. This article attempts to do these things for machine learning applied to music creation. Together with practitioners, we develop and use several applications of machine learning for music creation, and present a public concert of the results. We reflect on the entire experience to arrive at several ways of advancing these and similar applications of machine learning to music creation.","doi":"10.1080/09298215.2018.1515233","issn":"0929-8215","journal":"Journal of New Music Research","keywords":"Applied machine learning,computational creativity,folk music,music generation,music information retrieval","mendeley-tags":"music information retrieval","month":"jan","number":"1","pages":"36–55","url":"https://www.tandfonline.com/doi/full/10.1080/09298215.2018.1515233","volume":"48","bibtex":"@Article{ sturm.ea2019-machine,\n author = {Sturm, Bob L. and Ben-Tal, Oded and Monaghan, {\\'{U}}na\n and Collins, Nick and Herremans, Dorien and Chew, Elaine\n and Hadjeres, Ga{\\\"{e}}tan and Deruty, Emmanuel and\n Pachet, Fran{\\c{c}}ois},\n year = {2019},\n title = {Machine learning research that matters for music\n creation: A case study},\n abstract = {Research applying machine learning to music modelling and\n generation typically proposes model architectures,\n training methods and datasets, and gauges system\n performance using quantitative measures like sequence\n likelihoods and/or qualitative listening tests. Rarely\n does such work explicitly question and analyse its\n usefulness for and impact on real-world practitioners, and\n then build on those outcomes to inform the development and\n application of machine learning. This article attempts to\n do these things for machine learning applied to music\n creation. Together with practitioners, we develop and use\n several applications of machine learning for music\n creation, and present a public concert of the results. We\n reflect on the entire experience to arrive at several ways\n of advancing these and similar applications of machine\n learning to music creation.},\n doi = {10.1080/09298215.2018.1515233},\n issn = {0929-8215},\n journal = {Journal of New Music Research},\n keywords = {Applied machine learning,computational creativity,folk\n music,music generation,music information retrieval},\n mendeley-tags= {music information retrieval},\n month = {jan},\n number = {1},\n pages = {36--55},\n url = {https://www.tandfonline.com/doi/full/10.1080/09298215.2018.1515233},\n volume = {48}\n}\n\n","author_short":["Sturm, B. L.","Ben-Tal, O.","Monaghan, Ú.","Collins, N.","Herremans, D.","Chew, E.","Hadjeres, G.","Deruty, E.","Pachet, F."],"key":"sturm.ea2019-machine","id":"sturm.ea2019-machine","bibbaseid":"sturm-bental-monaghan-collins-herremans-chew-hadjeres-deruty-etal-machinelearningresearchthatmattersformusiccreationacasestudy-2019","role":"author","urls":{"Paper":"https://www.tandfonline.com/doi/full/10.1080/09298215.2018.1515233"},"keyword":["Applied machine learning","computational creativity","folk music","music generation","music information retrieval"],"metadata":{"authorlinks":{}},"downloads":0},"bibtype":"article","biburl":"https://hmb.sampaio.me/bibliografia.bib.txt","creationDate":"2020-10-09T14:50:13.861Z","downloads":0,"keywords":["applied machine learning","computational creativity","folk music","music generation","music information retrieval"],"search_terms":["machine","learning","research","matters","music","creation","case","study","sturm","ben-tal","monaghan","collins","herremans","chew","hadjeres","deruty","pachet"],"title":"Machine learning research that matters for music creation: A case study","year":2019,"dataSources":["n6MFY2CscQLDpJ7nT","RFLDZw5KyJdadDXDm"]}