{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:40:48Z","timestamp":1742920848373,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030729134"},{"type":"electronic","value":"9783030729141"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-72914-1_29","type":"book-chapter","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T07:05:39Z","timestamp":1617260739000},"page":"443-458","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Application for Evolutionary Music Composition Using Autoencoders"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9099-339X","authenticated-orcid":false,"given":"Robert Neil","family":"McArthur","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5683-7529","authenticated-orcid":false,"given":"Charles Patrick","family":"Martin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,4,2]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","unstructured":"Biles, J.A., Miranda, E.R.: Evolutionary Computation for Musical Tasks, pp. 28\u201351. Springer, London, London (2007). https:\/\/doi.org\/10.1007\/978-1-84628-600-1_2","DOI":"10.1007\/978-1-84628-600-1_2"},{"key":"29_CR2","doi-asserted-by":"publisher","unstructured":"Bontrager, P., Roy, A., Togelius, J., Memon, N., Ross, A.: Deepmasterprints: generating masterprints for dictionary attacks via latent variable evolution*. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1\u20139 (2018). https:\/\/doi.org\/10.1109\/BTAS.2018.8698539","DOI":"10.1109\/BTAS.2018.8698539"},{"key":"29_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/978-3-319-77583-8_18","volume-title":"Computational Intelligence in Music, Sound, Art and Design","author":"P Bontrager","year":"2018","unstructured":"Bontrager, P., Lin, W., Togelius, J., Risi, S.: Deep interactive evolution. In: Liapis, A., Romero Cardalda, J.J., Ek\u00e1rt, A. (eds.) EvoMUSART 2018. LNCS, vol. 10783, pp. 267\u2013282. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-77583-8_18"},{"key":"29_CR4","unstructured":"Choi, K., Hawthorne, C., Simon, I., Dinculescu, M., Engel, J.H.: Encoding musical style with transformer autoencoders. In: Proceedings of the 37th International Conference on Machine Learning, ICML. Proceedings of Machine Learning Research, vol. 119, pp. 1899\u20131908. PMLR (2020). http:\/\/proceedings.mlr.press\/v119\/choi20b.html"},{"key":"29_CR5","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/978-3-030-37548-5_16","volume-title":"Pattern Recogn. Artif. Intell.","author":"M Farzaneh","year":"2020","unstructured":"Farzaneh, M., Mahdian Toroghi, R.: Music generation using an interactive evolutionary algorithm. In: Djeddi, C., Jamil, A., Siddiqi, I. (eds.) Pattern Recogn. Artif. Intell., pp. 207\u2013217. Springer International Publishing, Cham (2020)"},{"key":"29_CR6","volume-title":"Generative Deep Learning: Teaching Machines to Paint. Compose, Write and Play","author":"D Foster","year":"2019","unstructured":"Foster, D.: Generative Deep Learning: Teaching Machines to Paint. Compose, Write and Play. O\u2019Reilly Media, Newton (2019)"},{"key":"29_CR7","unstructured":"HackerPoet: Composer (2018). https:\/\/github.com\/HackerPoet\/Composer"},{"key":"29_CR8","unstructured":"Hawthorne, C., et al.: Enabling factorized piano music modeling and generation with the MAESTRO dataset. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=r1lYRjC9F7"},{"key":"29_CR9","unstructured":"Hoover, A.K., Szerlip, P.A., Norton, M.E., Brindle, T.A., Merritt, Z., Stanley, K.O.: Generating a complete multipart musical composition from a single monophonic melody with functional scaffolding. In: Proceedings of the Third International Conference on Computational Creativity (ICCC-2012), pp. 111\u2013118 (2012)"},{"key":"29_CR10","unstructured":"Huang, C.Z.A., et al.: Music transformer: generating music with long-term structure. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=rJe4ShAcF7"},{"key":"29_CR11","doi-asserted-by":"publisher","unstructured":"Huang, Y.S., Yang, Y.H.: Pop music transformer: beat-based modeling and generation of expressive pop piano compositions. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 1180\u20131188. Association for Computing Machinery, New York, NY, USA (2020). https:\/\/doi.org\/10.1145\/3394171.3413671","DOI":"10.1145\/3394171.3413671"},{"key":"29_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-1-84628-600-1_1","volume-title":"An Introduction to Evolutionary Computing for Musicians","author":"P Husbands","year":"2007","unstructured":"Husbands, P., Copley, P., Eldridge, A., Mandelis, J., Miranda, E.R., Biles, J.A.: An Introduction to Evolutionary Computing for Musicians, pp. 1\u201321. Springer, London (2007). https:\/\/doi.org\/10.1007\/978-1-84628-600-1_1"},{"key":"29_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/978-3-642-36955-1_3","volume-title":"Evolutionary and Biologically Inspired Music, Sound, Art and Design","author":"MA Kaliakatsos\u2013Papakostas","year":"2013","unstructured":"Kaliakatsos\u2013Papakostas, M.A., Floros, A., Vrahatis, M.N.: evoDrummer: deriving rhythmic patterns through interactive genetic algorithms. In: Machado, P., McDermott, J., Carballal, A. (eds.) EvoMUSART 2013. LNCS, vol. 7834, pp. 25\u201336. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-36955-1_3"},{"issue":"1","key":"29_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40064-016-2398-8","volume":"5","author":"MA Kaliakatsos-Papakostas","year":"2016","unstructured":"Kaliakatsos-Papakostas, M.A., Floros, A., Vrahatis, M.N.: Interactive music composition driven by feature evolution. SpringerPlus 5(1), 1\u201338 (2016). https:\/\/doi.org\/10.1186\/s40064-016-2398-8","journal-title":"SpringerPlus"},{"key":"29_CR15","unstructured":"Newman, M., Evans, S., Carroll, M., Hill, J., Camarena, D.: Vgmusic (2020). https:\/\/vgmusic.com\/"},{"key":"29_CR16","first-page":"4364","volume-title":"Proceedings of the 35th International Conference on Machine Learning Proceedings of Machine Learning Research","author":"A Roberts","year":"2018","unstructured":"Roberts, A., Engel, J., Raffel, C., Hawthorne, C., Eck, D.: A hierarchical latent vector model for learning long-term structure in music. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning Proceedings of Machine Learning Research, pp. 4364\u20134373. PMLR Stockholmsm\u00e4ssan, Stockholm, Sweden (2018). http:\/\/proceedings.mlr.press\/v80\/roberts18a.html"},{"key":"29_CR17","unstructured":"Rogozinsky, G., Shchekochikhin, A.: On VAE latent space vectors distributed evolution driven music generation. In: Proceedings of the 11th Majorov International Conference on Software Engineering and Computer Systems. MICSECS (2019). http:\/\/ceur-ws.org\/Vol-2590\/paper22.pdf"},{"key":"29_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72877-1","volume-title":"The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music","author":"JJ Romero","year":"2008","unstructured":"Romero, J.J.: The Art of Artificial Evolution: A Handbook on Evolutionary Art and Music. Springer Science & Business Media, Berlin (2008)"},{"key":"29_CR19","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1007\/978-3-319-31008-4_14","volume-title":"Evolutionary and Biologically Inspired Music, Sound, Art and Design","author":"M Scirea","year":"2016","unstructured":"Scirea, M., Togelius, J., Eklund, P., Risi, S.: MetaCompose: a compositional evolutionary music composer. In: Johnson, C., Ciesielski, V., Correia, J., Machado, P. (eds.) Evolutionary and Biologically Inspired Music, Sound, Art and Design, pp. 202\u2013217. Springer International Publishing, Cham (2016)"},{"key":"29_CR20","doi-asserted-by":"publisher","unstructured":"Secretan, J., Beato, N., D Ambrosio, D.B., Rodriguez, A., Campbell, A., Stanley, K.O.: Picbreeder: evolving pictures collaboratively online. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1759\u20131768. CHI 2008, Association for Computing Machinery, New York, NY, USA (2008). https:\/\/doi.org\/10.1145\/1357054.1357328","DOI":"10.1145\/1357054.1357328"},{"key":"29_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-84628-600-1_6","volume-title":"Composing with Genetic Algorithms: GenDash","author":"R Waschka II","year":"2007","unstructured":"Waschka II, R., Miranda, E.R., Biles, J.A.: Composing with Genetic Algorithms: GenDash. Springer, London (2007). https:\/\/doi.org\/10.1007\/978-1-84628-600-1_6"},{"issue":"1","key":"29_CR22","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","volume":"2","author":"S Wold","year":"1987","unstructured":"Wold, S., Esbensen, K., Geladi, P.: Principal component analysis. Chemom. Intell. Lab. Syst. 2(1), 37\u201352 (1987). https:\/\/doi.org\/10.1016\/0169-7439(87)80084-9","journal-title":"Chemom. Intell. Lab. Syst."},{"issue":"12","key":"29_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42452-020-03715-w","volume":"2","author":"IP Yamshchikov","year":"2020","unstructured":"Yamshchikov, I.P., Tikhonov, A.: Music generation with variational recurrent autoencoder supported by history. SN Appl. Sci. 2(12), 1\u20137 (2020). https:\/\/doi.org\/10.1007\/s42452-020-03715-w","journal-title":"SN Appl. Sci."},{"issue":"9","key":"29_CR24","doi-asserted-by":"publisher","first-page":"4773","DOI":"10.1007\/s00521-018-3849-7","volume":"32","author":"L-C Yang","year":"2018","unstructured":"Yang, L.-C., Lerch, A.: On the evaluation of generative models in music. Neural Comput. Appl. 32(9), 4773\u20134784 (2018). https:\/\/doi.org\/10.1007\/s00521-018-3849-7","journal-title":"Neural Comput. Appl."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Music, Sound, Art and Design"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-72914-1_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:57:43Z","timestamp":1710341863000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72914-1_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030729134","9783030729141"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72914-1_29","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"2 April 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EvoMUSART","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Intelligence in Music, Sound, Art and Design (Part of EvoStar)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 April 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 April 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"evomusart2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.evostar.org\/2021\/evomusart\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"36% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Due to the Corona pandemic this event was held virtually.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}