{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:15:54Z","timestamp":1743131754864,"version":"3.40.3"},"publisher-location":"Cham","reference-count":39,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031569913"},{"type":"electronic","value":"9783031569920"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-56992-0_7","type":"book-chapter","created":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:02:01Z","timestamp":1711670521000},"page":"97-113","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MoodLoopGP: Generating Emotion-Conditioned Loop Tablature Music with\u00a0Multi-granular Features"],"prefix":"10.1007","author":[{"given":"Wenqian","family":"Cui","sequence":"first","affiliation":[]},{"given":"Pedro","family":"Sarmento","sequence":"additional","affiliation":[]},{"given":"Mathieu","family":"Barthet","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,29]]},"reference":[{"key":"7_CR1","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-031-29956-8_1","volume-title":"Artificial Intelligence in Music, Sound, Art and Design","author":"S Adkins","year":"2023","unstructured":"Adkins, S., Sarmento, P., Barthet, M.: LooperGP: a loopable sequence model for live coding performance using guitarpro tablature. In: Johnson, C., Rodr\u00edguez-Fern\u00e1ndez, N., Rebelo, S.M. (eds.) EvoMUSART 2023. LNCS, vol. 13988, pp. 3\u201319. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-29956-8_1"},{"key":"7_CR2","unstructured":"Alain, G., Chevalier-Boisvert, M., Osterrath, F., Piche-Taillefer, R.: Deepdrummer: generating drum loops using deep learning and a human in the loop. In: The 2020 Joint Conference on AI Music Creativity (2020)"},{"issue":"4","key":"7_CR3","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1038\/7299","volume":"2","author":"AJ Blood","year":"1999","unstructured":"Blood, A.J., Zatorre, R.J., Bermudez, P., Evans, A.C.: Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions. Nat. Neurosci. 2(4), 382\u2013387 (1999)","journal-title":"Nat. Neurosci."},{"issue":"12","key":"7_CR4","first-page":"141","volume":"10","author":"E Chew","year":"2014","unstructured":"Chew, E., et al.: Mathematical and computational modeling of tonality. AMC 10(12), 141 (2014)","journal-title":"AMC"},{"key":"7_CR5","doi-asserted-by":"publisher","unstructured":"Dai, Z., Yang, Z., Yang, Y., Carbonell, J., Le, Q., Salakhutdinov, R.: Transformer-XL: attentive language models beyond a fixed-length context. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2978\u20132988. Association for Computational Linguistics, Florence, Italy (2019). https:\/\/doi.org\/10.18653\/v1\/P19-1285, https:\/\/aclanthology.org\/P19-1285","DOI":"10.18653\/v1\/P19-1285"},{"issue":"3","key":"7_CR6","doi-asserted-by":"publisher","first-page":"B1","DOI":"10.1016\/S0010-0277(00)00136-0","volume":"80","author":"S Dalla Bella","year":"2001","unstructured":"Dalla Bella, S., Peretz, I., Rousseau, L., Gosselin, N.: A developmental study of the affective value of tempo and mode in music. Cognition 80(3), B1\u2013B10 (2001)","journal-title":"Cognition"},{"issue":"4","key":"7_CR7","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1177\/0305735610378182","volume":"39","author":"H Daynes","year":"2011","unstructured":"Daynes, H.: Listeners\u2019 perceptual and emotional responses to tonal and atonal music. Psychol. Music 39(4), 468\u2013502 (2011)","journal-title":"Psychol. Music"},{"key":"7_CR8","doi-asserted-by":"publisher","first-page":"80","DOI":"10.3389\/fncom.2016.00080","volume":"10","author":"A Fern\u00e1ndez-Sotos","year":"2016","unstructured":"Fern\u00e1ndez-Sotos, A., Fern\u00e1ndez-Caballero, A., Latorre, J.M.: Influence of tempo and rhythmic unit in musical emotion regulation. Front. Comput. Neurosci. 10, 80 (2016)","journal-title":"Front. Comput. Neurosci."},{"key":"7_CR9","unstructured":"Ferreira, L.N., Whitehead, J.: Learning to generate music with sentiment. In: Proceedings of the 20th International Society for Music Information Retrieval Conference, pp. 384\u2013390 (2019)"},{"key":"7_CR10","doi-asserted-by":"publisher","first-page":"129088","DOI":"10.1109\/ACCESS.2021.3113829","volume":"9","author":"J Grekow","year":"2021","unstructured":"Grekow, J., Dimitrova-Grekow, T.: Monophonic music generation with a given emotion using conditional variational autoencoder. IEEE Access 9, 129088\u2013129101 (2021)","journal-title":"IEEE Access"},{"key":"7_CR11","unstructured":"Han, S., Ihm, H., Lee, M., Lim, W.: Symbolic music loop generation with neural discrete representations. Proceedings of the 23th International Society for Music Information Retrieval Conference (2022)"},{"key":"7_CR12","unstructured":"Han, S., Ihm, H., Lim, W.: Symbolic music loop generation with VQ-VAE. arXiv preprint arXiv:2111.07657 (2021)"},{"key":"7_CR13","unstructured":"Herremans, D., Chew, E., et al.: Tension ribbons: quantifying and visualising tonal tension. (2016)"},{"issue":"3","key":"7_CR14","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1109\/6046.944475","volume":"3","author":"JL Hsu","year":"2001","unstructured":"Hsu, J.L., Liu, C.C., Chen, A.L.: Discovering nontrivial repeating patterns in music data. IEEE Trans. Multimedia 3(3), 311\u2013325 (2001)","journal-title":"IEEE Trans. Multimedia"},{"key":"7_CR15","doi-asserted-by":"crossref","unstructured":"Huang, C.F., Huang, C.Y.: Emotion-based AI music generation system with CVAE-GAN. In: 2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), pp. 220\u2013222. IEEE (2020)","DOI":"10.1109\/ECICE50847.2020.9301934"},{"key":"7_CR16","unstructured":"Hung, T.M., Chen, B.Y., Yeh, Y.T., Yang, Y.H.: A benchmarking initiative for audio-domain music generation using the freesound loop dataset. Proceedings of the 22th International Society for Music Information Retrieval Conference (2021)"},{"issue":"3","key":"7_CR17","doi-asserted-by":"publisher","first-page":"270","DOI":"10.1109\/TG.2019.2921979","volume":"12","author":"PE Hutchings","year":"2019","unstructured":"Hutchings, P.E., McCormack, J.: Adaptive music composition for games. IEEE Trans. Games 12(3), 270\u2013280 (2019)","journal-title":"IEEE Trans. Games"},{"issue":"6","key":"7_CR18","doi-asserted-by":"publisher","first-page":"1797","DOI":"10.1037\/0096-1523.26.6.1797","volume":"26","author":"PN Juslin","year":"2000","unstructured":"Juslin, P.N.: Cue utilization in communication of emotion in music performance: relating performance to perception. J. Exp. Psychol. Hum. Percept. Perform. 26(6), 1797 (2000)","journal-title":"J. Exp. Psychol. Hum. Percept. Perform."},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Kalansooriya, P., Ganepola, G.D., Thalagala, T.: Affective gaming in real-time emotion detection and smart computing music emotion recognition: implementation approach with electroencephalogram. In: 2020 International Research Conference on Smart Computing and Systems Engineering (SCSE), pp. 111\u2013116. IEEE (2020)","DOI":"10.1109\/SCSE49731.2020.9313028"},{"key":"7_CR20","unstructured":"Keskar, N.S., McCann, B., Varshney, L.R., Xiong, C., Socher, R.: Ctrl: a conditional transformer language model for controllable generation. arXiv preprint arXiv:1909.05858 (2019)"},{"key":"7_CR21","unstructured":"Loth, J., Sarmento, P., Carr, C., Zukowski, Z., Barthet, M.: Proggp: from guitarpro tablature neural generation to progressive metal production. The 16th International Symposium on Computer Music Multidisciplinary Research (2023)"},{"key":"7_CR22","doi-asserted-by":"crossref","unstructured":"Madhok, R., Goel, S., Garg, S.: Sentimozart: music generation based on emotions. In: ICAART (2), pp. 501\u2013506 (2018)","DOI":"10.5220\/0006597705010506"},{"key":"7_CR23","doi-asserted-by":"crossref","unstructured":"McVicar, M., Fukayama, S., Goto, M.: Autoleadguitar: automatic generation of guitar solo phrases in the tablature space. In: 2014 12th International Conference on Signal Processing (ICSP), pp. 599\u2013604. IEEE (2014)","DOI":"10.1109\/ICOSP.2014.7015074"},{"key":"7_CR24","unstructured":"Panda, R., Redinho, H., Gon\u00e7alves, C., Malheiro, R., Paiva, R.P.: How does the spotify api compare to the music emotion recognition state-of-the-art? In: 18th Sound and Music Computing Conference (SMC 2021), pp. 238\u2013245. Axea sas\/SMC Network (2021)"},{"key":"7_CR25","unstructured":"Ruiguo-Bio: Ruiguo-bio\/midi-miner: Python midi track classifier and tonal tension calculation based on spiral array theory (2023). https:\/\/github.com\/ruiguo-bio\/midi-miner"},{"issue":"6","key":"7_CR26","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1037\/h0077714","volume":"39","author":"JA Russell","year":"1980","unstructured":"Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161 (1980)","journal-title":"J. Pers. Soc. Psychol."},{"key":"7_CR27","unstructured":"Sarmento, P., Holmqvist, O., Barthet, M., et al.: Ubiquitous music in smart city: musification of air pollution and user context (2022)"},{"key":"7_CR28","unstructured":"Sarmento, P., Kumar, A., Carr, C., Zukowski, Z., Barthet, M., Yang, Y.H.: DadaGP: a dataset of tokenized guitarpro songs for sequence models. In: Proceedings of the 22th International Society for Music Information Retrieval Conference, pp. 610\u2013618 (2021)"},{"key":"7_CR29","series-title":"LNCS","doi-asserted-by":"publisher","first-page":"260","DOI":"10.1007\/978-3-031-29956-8_17","volume-title":"Artificial Intelligence in Music, Sound, Art and Design","author":"P Sarmento","year":"2023","unstructured":"Sarmento, P., Kumar, A., Chen, Y.H., Carr, C., Zukowski, Z., Barthet, M.: GTR-CTRL: instrument and genre conditioning for guitar-focused music generation with transformers. In: Johnson, C., Rodr\u00edguez-Fern\u00e1ndez, N., Rebelo, S.M. (eds.) EvoMUSART 2023. LNCS, vol. 13988, pp. 260\u2013275. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-29956-8_17"},{"key":"7_CR30","unstructured":"Sarmento, P., Kumar, A., Xie, D., Carr, C., Zukowski, Z., Barthet, M.: Shredgp: guitarist style-conditioned tablature generation. In: Proceedings of the 16th International Symposium on Computer Music Multidisciplinary Research (CMMR) 2023. (2023)"},{"key":"7_CR31","doi-asserted-by":"publisher","first-page":"44617","DOI":"10.1109\/ACCESS.2022.3169744","volume":"10","author":"S Sulun","year":"2022","unstructured":"Sulun, S., Davies, M.E., Viana, P.: Symbolic music generation conditioned on continuous-valued emotions. IEEE Access 10, 44617\u201344626 (2022)","journal-title":"IEEE Access"},{"key":"7_CR32","unstructured":"Takahashi, T., Barthet, M.: Emotion-driven harmonisation and tempo arrangement of melodies using transfer learning"},{"key":"7_CR33","unstructured":"Tan, H.H., Herremans, D.: Music fadernets: controllable music generation based on high-level features via low-level feature modelling. Proceedings of the 21th International Society for Music Information Retrieval Conference (2020)"},{"key":"7_CR34","unstructured":"Tan, X., Antony, M., Kong, H.: Automated music generation for visual art through emotion. In: ICCC, pp. 247\u2013250 (2020)"},{"key":"7_CR35","unstructured":"Tripodi, I.J.: Setting the rhythm scene: deep learning-based drum loop generation from arbitrary language cues. arXiv preprint arXiv:2209.10016 (2022)"},{"key":"7_CR36","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/s11031-005-4414-0","volume":"29","author":"GD Webster","year":"2005","unstructured":"Webster, G.D., Weir, C.G.: Emotional responses to music: interactive effects of mode, texture, and tempo. Motiv. Emot. 29, 19\u201339 (2005)","journal-title":"Motiv. Emot."},{"issue":"6","key":"7_CR37","doi-asserted-by":"publisher","first-page":"831","DOI":"10.1177\/0305735614543282","volume":"43","author":"D Williams","year":"2015","unstructured":"Williams, D., Kirke, A., Miranda, E.R., Roesch, E., Daly, I., Nasuto, S.: Investigating affect in algorithmic composition systems. Psychol. Music 43(6), 831\u2013854 (2015)","journal-title":"Psychol. Music"},{"issue":"02","key":"7_CR38","doi-asserted-by":"publisher","first-page":"1442","DOI":"10.1109\/TAFFC.2021.3093787","volume":"14","author":"S Yang","year":"2023","unstructured":"Yang, S., Reed, C.N., Chew, E., Barthet, M.: Examining emotion perception agreement in live music performance. IEEE Trans. Affect. Comput. 14(02), 1442\u20131460 (2023). https:\/\/doi.org\/10.1109\/TAFFC.2021.3093787","journal-title":"IEEE Trans. Affect. Comput."},{"key":"7_CR39","unstructured":"Yeh, Y.T., Chen, B.Y., Yang, Y.H.: Exploiting pre-trained feature networks for generative adversarial networks in audio-domain loop generation. In: Proceedings of the 23th International Society for Music Information Retrieval Conference (2022)"}],"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\/978-3-031-56992-0_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,29]],"date-time":"2024-03-29T00:03:24Z","timestamp":1711670604000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56992-0_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031569913","9783031569920"],"references-count":39,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56992-0_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"29 March 2024","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":"Aberystwyth","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 April 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 April 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"evomusart2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.evostar.org\/2024\/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":"55","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":"17","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":"8","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":"31% - 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)"}}]}}