{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,3]],"date-time":"2025-05-03T04:15:22Z","timestamp":1746245722303,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"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_12","type":"book-chapter","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T07:05:39Z","timestamp":1617260739000},"page":"171-186","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Chord Embeddings: Analyzing What They Capture and Their Role for Next Chord Prediction and Artist Attribute Prediction"],"prefix":"10.1007","author":[{"given":"Allison","family":"Lahnala","sequence":"first","affiliation":[]},{"given":"Gauri","family":"Kambhatla","sequence":"additional","affiliation":[]},{"given":"Jiajun","family":"Peng","sequence":"additional","affiliation":[]},{"given":"Matthew","family":"Whitehead","sequence":"additional","affiliation":[]},{"given":"Gillian","family":"Minnehan","sequence":"additional","affiliation":[]},{"given":"Eric","family":"Guldan","sequence":"additional","affiliation":[]},{"given":"Jonathan K.","family":"Kummerfeld","sequence":"additional","affiliation":[]},{"given":"An\u0131l","family":"\u00c7amc\u0131","sequence":"additional","affiliation":[]},{"given":"Rada","family":"Mihalcea","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,4,2]]},"reference":[{"key":"12_CR1","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-642-11674-2_8","volume-title":"Advances in Music Information Retrieval","author":"B Absolu","year":"2010","unstructured":"Absolu, B., Li, T., Ogihara, M.: Analysis of chord progression data. In: Ra\u015b, Z.W., Wieczorkowska, A.A. (eds.) Advances in Music Information Retrieval. Studies in Computational Intelligence, vol. 274, pp. 165\u2013184. Springer, Berlin (2010). https:\/\/doi.org\/10.1007\/978-3-642-11674-2_8"},{"key":"12_CR2","unstructured":"Brinkman, A., Shanahan, D., Sapp, C.: Musical stylometry, machine learning and attribution studies: a semi-supervised approach to the works of Josquin. In: Proceedings of the Biennial International Conference on Music Perception and Cognition, pp. 91\u201397 (2016)"},{"key":"12_CR3","doi-asserted-by":"crossref","unstructured":"Brunner, G., Wang, Y., Wattenhofer, R., Wiesendanger, J.: JamBot: music theory aware chord based generation of polyphonic music with LSTMs. In: Proceedings of ICTAI (2017)","DOI":"10.1109\/ICTAI.2017.00085"},{"key":"12_CR4","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL (2019)"},{"issue":"1","key":"12_CR5","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1146\/annurev.an.23.100194.000325","volume":"23","author":"S Feld","year":"1994","unstructured":"Feld, S., Fox, A.A.: Music and language. Annu. Rev. Anthropol. 23(1), 25\u201353 (1994)","journal-title":"Annu. Rev. Anthropol."},{"key":"12_CR6","unstructured":"Fell, M., Sporleder, C.: Lyrics-based analysis and classification of music. In: Proceedings of COLING (2014)"},{"key":"12_CR7","doi-asserted-by":"crossref","unstructured":"Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: Proceedings of KDD (2016)","DOI":"10.1145\/2939672.2939754"},{"key":"12_CR8","unstructured":"Hillewaere, R., Manderick, B., Conklin, D.: Melodic models for polyphonic music classification. In: Second International Workshop on Machine Learning and Music (2009)"},{"issue":"8","key":"12_CR9","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"12_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-642-38628-2_88","volume-title":"Pattern Recognition and Image Analysis","author":"M Hontanilla","year":"2013","unstructured":"Hontanilla, M., P\u00e9rez-Sancho, C., I\u00f1esta, J.M.: Modeling musical style with language models for composer recognition. In: Sanches, J.M., Mic\u00f3, L., Cardoso, J.S. (eds.) IbPRIA 2013. LNCS, vol. 7887, pp. 740\u2013748. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-38628-2_88"},{"key":"12_CR11","doi-asserted-by":"publisher","first-page":"123","DOI":"10.3389\/fpsyg.2012.00123","volume":"3","author":"L J\u00e4ncke","year":"2012","unstructured":"J\u00e4ncke, L.: The relationship between music and language. Front. Psychol. 3, 123 (2012)","journal-title":"Front. Psychol."},{"key":"12_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1007\/978-3-642-12242-2_42","volume-title":"Applications of Evolutionary Computation","author":"MA Kaliakatsos-Papakostas","year":"2010","unstructured":"Kaliakatsos-Papakostas, M.A., Epitropakis, M.G., Vrahatis, M.N.: Musical composer identification through probabilistic and feedforward neural networks. In: Di Chio, C., et al. (eds.) EvoApplications 2010. LNCS, vol. 6025, pp. 411\u2013420. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-12242-2_42"},{"key":"12_CR13","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746\u20131751 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"12_CR14","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR 2015 (2014)"},{"key":"12_CR15","unstructured":"Madjiheurem, S., Qu, L., Walder, C.: Chord2vec: learning musical chord embeddings. In: Proceedings of the Constructive Machine Learning Workshop (2016)"},{"key":"12_CR16","unstructured":"Mayer, R., Rauber, A.: Musical genre classification by ensembles of audio and lyrics features. In: Proceedings of ISMIR (2011)"},{"issue":"2","key":"12_CR17","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s10579-009-9084-1","volume":"43","author":"D McCarthy","year":"2009","unstructured":"McCarthy, D., Navigli, R.: The English lexical substitution task. Lang. Resour. Eval. 43(2), 139\u2013159 (2009). https:\/\/doi.org\/10.1007\/s10579-009-9084-1","journal-title":"Lang. Resour. Eval."},{"key":"12_CR18","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: ICLR (2013)"},{"key":"12_CR19","unstructured":"Ogihara, M., Li, T.: N-gram chord profiles for composer style representation. In: ISMIR, pp. 671\u2013676 (2008)"},{"key":"12_CR20","volume-title":"Music Theory Resource Book","author":"H Owen","year":"2000","unstructured":"Owen, H.: Music Theory Resource Book. Oxford University Press, USA (2000)"},{"issue":"7","key":"12_CR21","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1038\/nn1082","volume":"6","author":"AD Patel","year":"2003","unstructured":"Patel, A.D.: Language, music, syntax and the brain. Nat. Neurosci. 6(7), 674 (2003)","journal-title":"Nat. Neurosci."},{"key":"12_CR22","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of EMNLP (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"12_CR23","doi-asserted-by":"crossref","unstructured":"Peters, M., et al.: Deep contextualized word representations. In: Proceedings of NAACL (2018)","DOI":"10.18653\/v1\/N18-1202"},{"key":"12_CR24","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-981-32-9563-6_1","volume-title":"Data Mining and Big Data","author":"S Phon-Amnuaisuk","year":"2019","unstructured":"Phon-Amnuaisuk, S.: Exploring Music21 and Gensim for music data analysis and visualization. In: Tan, Y., Shi, Y. (eds.) DMBD 2019. CCIS, vol. 1071, pp. 3\u201312. Springer, Singapore (2019). https:\/\/doi.org\/10.1007\/978-981-32-9563-6_1"},{"key":"12_CR25","volume-title":"The Harvard Concise Dictionary of Music and Musicians","author":"DM Randel","year":"1999","unstructured":"Randel, D.M.: The Harvard Concise Dictionary of Music and Musicians. Harvard University Press, Cambridge (1999)"},{"key":"12_CR26","unstructured":"Saker, M.N.: A theory of circle of fifths progressions and their application in the four Ballades by Frederic Chopin. Ph.D. thesis, University of Wisconsin-Madison (1992)"},{"key":"12_CR27","doi-asserted-by":"publisher","unstructured":"Sergeant, D.C., Himonides, E.: Gender and music composition: a study of music, and the gendering of meanings. Front. Psychol. 7, 411 (2016). https:\/\/doi.org\/10.3389\/fpsyg.2016.00411, https:\/\/www.frontiersin.org\/article\/10.3389\/fpsyg.2016.00411","DOI":"10.3389\/fpsyg.2016.00411"},{"key":"12_CR28","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1017\/S0261143000001276","volume":"2","author":"J Shepherd","year":"1982","unstructured":"Shepherd, J.: A theoretical model for the sociomusicological analysis of popular musics. Popular Music 2, 145\u2013177 (1982)","journal-title":"Popular Music"},{"key":"12_CR29","unstructured":"Shuyo, N.: Language detection library for java (2010). http:\/\/code.google.com\/p\/language-detection\/"},{"key":"12_CR30","doi-asserted-by":"publisher","unstructured":"Stamatatos, E.: A survey of modern authorship attribution methods. J. Am. Soc. Inf. Sci. Technol. 60(3), 538\u2013556 (2009). https:\/\/doi.org\/10.1002\/asi.21001, https:\/\/onlinelibrary.wiley.com\/doi\/abs\/10.1002\/asi.21001","DOI":"10.1002\/asi.21001"},{"issue":"1","key":"12_CR31","first-page":"43","volume":"33","author":"J Wo\u0142kowicz","year":"2008","unstructured":"Wo\u0142kowicz, J., Kulka, Z., Ke\u0161elj, V.: N-gram-based approach to composer recognition. Arch. Acoust. 33(1), 43\u201355 (2008)","journal-title":"Arch. Acoust."}],"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_12","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:53:12Z","timestamp":1710341592000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-72914-1_12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030729134","9783030729141"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-72914-1_12","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)"}}]}}