{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T12:35:16Z","timestamp":1763728516217,"version":"3.40.3"},"publisher-location":"Cham","reference-count":71,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031368042"},{"type":"electronic","value":"9783031368059"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-36805-9_25","type":"book-chapter","created":{"date-parts":[[2023,6,29]],"date-time":"2023-06-29T23:03:04Z","timestamp":1688079784000},"page":"366-384","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Computational Music: Analysis of\u00a0Music Forms"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7743-1947","authenticated-orcid":false,"given":"Jing","family":"Zhao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4893-2291","authenticated-orcid":false,"given":"KokSheik","family":"Wong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6809-5817","authenticated-orcid":false,"given":"Vishnu Monn","family":"Baskaran","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5884-1409","authenticated-orcid":false,"given":"Kiki","family":"Adhinugraha","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8862-3960","authenticated-orcid":false,"given":"David","family":"Taniar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,30]]},"reference":[{"issue":"11","key":"25_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2018.e00938","volume":"4","author":"OI Abiodun","year":"2018","unstructured":"Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A., Arshad, H.: State-of-the-art in artificial neural network applications: a survey. Heliyon 4(11), e00938 (2018)","journal-title":"Heliyon"},{"issue":"1","key":"25_CR2","doi-asserted-by":"publisher","first-page":"82","DOI":"10.5334\/tismir.27","volume":"2","author":"P Allegraud","year":"2019","unstructured":"Allegraud, P., et al.: Learning sonata form structure on mozart\u2019s string quartets. Trans. Int. Society Music Inform. Retrieval (TISMIR) 2(1), 82\u201396 (2019)","journal-title":"Trans. Int. Society Music Inform. Retrieval (TISMIR)"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Anagnostopoulou, C., Buteau, C.: Can computational music analysis be both musical and computational? J. Math. Music 4(2), 75\u201383 (2010)","DOI":"10.1080\/17459737.2010.520455"},{"key":"25_CR4","unstructured":"Arnold, J.M.: The role of chromaticism in Chopin\u2019s sonata forms: a Schenkerian view. Northwestern University (1992)"},{"key":"25_CR5","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1016\/j.future.2020.08.005","volume":"115","author":"ME Basiri","year":"2021","unstructured":"Basiri, M.E., Nemati, S., Abdar, M., Cambria, E., Acharya, U.R.: Abcdm: an attention-based bidirectional cnn-rnn deep model for sentiment analysis. Futur. Gener. Comput. Syst. 115, 279\u2013294 (2021)","journal-title":"Futur. Gener. Comput. Syst."},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Bergstrom, T., Karahalios, K., Hart, J.C.: Isochords: visualizing structure in music. In: Proceedings of Graphics Interface 2007, pp. 297\u2013304 (2007)","DOI":"10.1145\/1268517.1268565"},{"key":"25_CR7","unstructured":"Bigo, L., Giraud, M., Groult, R., Guiomard-Kagan, N., Lev\u00e9, F.: Sketching sonata form structure in selected classical string quartets. In: ISMIR 2017-International Society for Music Information Retrieval Conference (2017)"},{"key":"25_CR8","doi-asserted-by":"crossref","unstructured":"Buccoli, M., Zanoni, M., Sarti, A., Tubaro, S., Andreoletti, D.: Unsupervised feature learning for music structural analysis. In: 2016 24th European Signal Processing Conference (EUSIPCO), pp. 993\u2013997. IEEE (2016)","DOI":"10.1109\/EUSIPCO.2016.7760397"},{"key":"25_CR9","unstructured":"Buisson, M., Mcfee, B., Essid, S., Crayencour, H.C.: Learning multi-level representations for hierarchical music structure analysis. In: International Society for Music Information Retrieval (ISMIR) (2022)"},{"key":"25_CR10","doi-asserted-by":"crossref","unstructured":"Burgoyne, J.A., Fujinaga, I., Downie, J.S.: Music information retrieval. A new companion to digital humanities, pp. 213\u2013228 (2015)","DOI":"10.1002\/9781118680605.ch15"},{"issue":"8","key":"25_CR11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0070252","volume":"8","author":"C Carr","year":"2013","unstructured":"Carr, C., Odell-Miller, H., Priebe, S.: A systematic review of music therapy practice and outcomes with acute adult psychiatric in-patients. PLoS ONE 8(8), e70252 (2013)","journal-title":"PLoS ONE"},{"issue":"1","key":"25_CR12","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1109\/TVCG.2009.63","volume":"16","author":"WY Chan","year":"2009","unstructured":"Chan, W.Y., Qu, H., Mak, W.H.: Visualizing the semantic structure in classical music works. IEEE Trans. Visual Comput. Graphics 16(1), 161\u2013173 (2009)","journal-title":"IEEE Trans. Visual Comput. Graphics"},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"Chawin, D., Rom, U.B.: Sliding-window pitch-class histograms as a means of modeling musical form. Trans. Int. Society for Music Inform. Retrieval 4(1), (2021)","DOI":"10.5334\/tismir.83"},{"key":"25_CR14","doi-asserted-by":"crossref","unstructured":"Chen, P., Zhao, L., Xin, Z., Qiang, Y., Zhang, M., Li, T.: A scheme of midi music emotion classification based on fuzzy theme extraction and neural network. In: 2016 12th International Conference on Computational Intelligence and Security (CIS), pp. 323\u2013326. IEEE (2016)","DOI":"10.1109\/CIS.2016.0079"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Cheng, T., Smith, J.B., Goto, M.: Music structure boundary detection and labelling by a deconvolution of path-enhanced self-similarity matrix. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 106\u2013110. IEEE (2018)","DOI":"10.1109\/ICASSP.2018.8461319"},{"key":"25_CR16","doi-asserted-by":"publisher","unstructured":"Chew, E.: Cosmos: Computational shaping and modeling of musical structures. Front. Psychol. 13 (2022). https:\/\/doi.org\/10.3389\/fpsyg.2022.527539","DOI":"10.3389\/fpsyg.2022.527539"},{"issue":"5","key":"25_CR17","first-page":"851","volume":"6","author":"S Chillara","year":"2019","unstructured":"Chillara, S., Kavitha, A., Neginhal, S.A., Haldia, S., Vidyullatha, K.: Music genre classification using machine learning algorithms: a comparison. Int. Res. J. Eng. Technol. 6(5), 851\u2013858 (2019)","journal-title":"Int. Res. J. Eng. Technol."},{"key":"25_CR18","doi-asserted-by":"crossref","unstructured":"Clercq, T.d.: Embracing ambiguity in the analysis of form in pop\/rock music, 1982\u20131991. Music Theory Online 23(3), (2017)","DOI":"10.30535\/mto.23.3.4"},{"key":"25_CR19","first-page":"93","volume":"4","author":"GE Corazza","year":"2014","unstructured":"Corazza, G.E., Agnoli, S., Martello, S.: Counterpoint as a principle of creativity: extracting divergent modifiers from\u2019the art of fugue\u2019by johann sebastian bach. Musica Docta 4, 93\u2013105 (2014)","journal-title":"Musica Docta"},{"key":"25_CR20","unstructured":"Dai, S., Jin, Z., Gomes, C., Dannenberg, R.B.: Controllable deep melody generation via hierarchical music structure representation. arXiv preprint arXiv:2109.00663 (2021)"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"De Prisco, R., et al: Music plagiarism at a glance: metrics of similarity and visualizations. In: 2017 21st International Conference Information Visualisation (IV), pp. 410\u2013415. IEEE (2017)","DOI":"10.1109\/iV.2017.49"},{"issue":"2","key":"25_CR22","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1177\/1473871616655468","volume":"16","author":"R De Prisco","year":"2017","unstructured":"De Prisco, R., Malandrino, D., Pirozzi, D., Zaccagnino, G., Zaccagnino, R.: Understanding the structure of musical compositions: is visualization an effective approach? Inf. Vis. 16(2), 139\u2013152 (2017)","journal-title":"Inf. Vis."},{"issue":"4","key":"25_CR23","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1093\/ml\/17.4.309","volume":"17","author":"EJ Dent","year":"1936","unstructured":"Dent, E.J.: Binary and ternary form. Music Lett. 17(4), 309\u2013321 (1936)","journal-title":"Music Lett."},{"key":"25_CR24","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"25_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11910-019-1005-0","volume":"19","author":"K Devlin","year":"2019","unstructured":"Devlin, K., Alshaikh, J.T., Pantelyat, A.: Music therapy and music-based interventions for movement disorders. Curr. Neurol. Neurosci. Rep. 19, 1\u201313 (2019)","journal-title":"Curr. Neurol. Neurosci. Rep."},{"key":"25_CR26","unstructured":"Dirst, M., Weigend, A.S.: On completing js bach\u2019s last fugue. Time Series Prediction: Forecasting the Future and Understanding the Past, pp. 151\u2013177 (1994)"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Fuentes, M., McFee, B., Crayencour, H.C., Essid, S., Bello, J.P.: A music structure informed downbeat tracking system using skip-chain conditional random fields and deep learning. In: ICASSP 2019\u20132019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 481\u2013485. IEEE (2019)","DOI":"10.1109\/ICASSP.2019.8682870"},{"issue":"2","key":"25_CR28","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1162\/COMJ_a_00300","volume":"39","author":"M Giraud","year":"2015","unstructured":"Giraud, M., Groult, R., Leguy, E., Lev\u00e9, F.: Computational fugue analysis. Comput. Music. J. 39(2), 77\u201396 (2015)","journal-title":"Comput. Music. J."},{"key":"25_CR29","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1007\/978-3-642-41248-6_24","volume-title":"From Sounds to Music and Emotions","author":"M Giraud","year":"2013","unstructured":"Giraud, M., Groult, R., Lev\u00e9, F.: Subject and counter-subject detection for analysis of the well-tempered clavier fugues. In: Aramaki, M., Barthet, M., Kronland-Martinet, R., Ystad, S. (eds.) CMMR 2012. LNCS, vol. 7900, pp. 422\u2013438. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-41248-6_24"},{"issue":"2","key":"25_CR30","doi-asserted-by":"publisher","first-page":"78","DOI":"10.9781\/ijimai.2021.10.005","volume":"7","author":"C Hernandez-Olivan","year":"2021","unstructured":"Hernandez-Olivan, C., Beltran, J.R., Diaz-Guerra, D.: Music boundary detection using convolutional neural networks: a comparative analysis of combined input features. Int. J. Interact. Multimedia Artif. Intell. 7(2), 78 (2021). https:\/\/doi.org\/10.9781\/ijimai.2021.10.005","journal-title":"Int. J. Interact. Multimedia Artif. Intell."},{"key":"25_CR31","unstructured":"Huang, C.Z.A., Cooijmans, T., Roberts, A., Courville, A., Eck, D.: Counterpoint by convolution. arXiv preprint arXiv:1903.07227 (2019)"},{"issue":"1","key":"25_CR32","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.cognition.2005.11.005","volume":"100","author":"R Jackendoff","year":"2006","unstructured":"Jackendoff, R., Lerdahl, F.: The capacity for music: what is it, and what\u2019s special about it? Cognition 100(1), 33\u201372 (2006)","journal-title":"Cognition"},{"key":"25_CR33","doi-asserted-by":"crossref","unstructured":"Jain, A., Zamir, A.R., Savarese, S., Saxena, A.: Structural-rnn: Deep learning on spatio-temporal graphs. In: Proceedings of The IEEE Conference On Computer Vision And Pattern Recognition, pp. 5308\u20135317 (2016)","DOI":"10.1109\/CVPR.2016.573"},{"key":"25_CR34","doi-asserted-by":"publisher","first-page":"1893","DOI":"10.1007\/s11063-020-10241-8","volume":"52","author":"C Jin","year":"2020","unstructured":"Jin, C., Tie, Y., Bai, Y., Lv, X., Liu, S.: A style-specific music composition neural network. Neural Process. Lett. 52, 1893\u20131912 (2020)","journal-title":"Neural Process. Lett."},{"key":"25_CR35","doi-asserted-by":"crossref","unstructured":"Jun, S., Hwang, E.: Music segmentation and summarization based on self-similarity matrix. In: Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication, p. 4. No. 82 in ICUIMC \u201913, Association for Computing Machinery, New York, NY, USA (2013)","DOI":"10.1145\/2448556.2448638"},{"key":"25_CR36","doi-asserted-by":"crossref","unstructured":"Kao, W.T., Lee, H.Y.: Is bert a cross-disciplinary knowledge learner? a surprising finding of pre-trained models\u2019 transferability. arXiv preprint arXiv:2103.07162 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.189"},{"issue":"3","key":"25_CR37","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1080\/08098131.2020.1728781","volume":"29","author":"J Kenner","year":"2020","unstructured":"Kenner, J., Baker, F.A., Treloyn, S.: Perspectives on musical competence for people with borderline personality disorder in group music therapy. Nord. J. Music. Ther. 29(3), 271\u2013287 (2020)","journal-title":"Nord. J. Music. Ther."},{"key":"25_CR38","doi-asserted-by":"crossref","unstructured":"Kumar, C., Dutta, S., Chakborty, S.: Musical cryptography using genetic algorithm. In: 2014 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2014], pp. 1742\u20131747. IEEE (2014)","DOI":"10.1109\/ICCPCT.2014.7054851"},{"key":"25_CR39","doi-asserted-by":"crossref","unstructured":"Lawes, M.: Creating a covid-19 guided imagery and music (gim) self-help resource for those with mild to moderate symptoms of the disease. Approaches: An Interdisciplinary Journal of Music Therapy, pp. 1\u201317 (2020)","DOI":"10.56883\/aijmt.2022.125"},{"issue":"7553","key":"25_CR40","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"2","key":"25_CR41","doi-asserted-by":"publisher","first-page":"235","DOI":"10.2307\/843876","volume":"42","author":"D Lewin","year":"1998","unstructured":"Lewin, D.: Notes on the opening of the f# minor fugue from wtci. J. Music Theor. 42(2), 235\u2013239 (1998)","journal-title":"J. Music Theor."},{"issue":"4","key":"25_CR42","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1162\/COLI_a_00239","volume":"41","author":"CD Manning","year":"2015","unstructured":"Manning, C.D.: Computational linguistics and deep learning. Comput. Linguist. 41(4), 701\u2013707 (2015)","journal-title":"Comput. Linguist."},{"key":"25_CR43","doi-asserted-by":"publisher","first-page":"7451","DOI":"10.1007\/s11042-020-09962-8","volume":"80","author":"YMH Marandi","year":"2021","unstructured":"Marandi, Y.M.H., Sajedi, H., Pirasteh, S.: A novel method to musicalize shape and visualize music and a novel technique in music cryptography. Multimedia Tools Appl. 80, 7451\u20137477 (2021)","journal-title":"Multimedia Tools Appl."},{"key":"25_CR44","doi-asserted-by":"crossref","unstructured":"Marr, D.: Vision: A computational investigation into the human representation and processing of visual information. MIT press (2010)","DOI":"10.7551\/mitpress\/9780262514620.001.0001"},{"key":"25_CR45","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-25931-4_1","volume-title":"Computational Music Analysis","author":"Alan Marsden","year":"2016","unstructured":"Marsden, Alan: Music analysis by computer: ontology and epistemology. In: Computational Music Analysis, pp. 3\u201328. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-25931-4_1"},{"key":"25_CR46","unstructured":"Mauch, M., Levy, M.: Structural change on multiple time scales as a correlate of musical complexity, pp. 489\u2013494 (01 2011)"},{"issue":"3","key":"25_CR47","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1080\/09298215.2015.1045003","volume":"44","author":"D Meredith","year":"2015","unstructured":"Meredith, D.: Music analysis and point-set compression. J. New Music Res. 44(3), 245\u2013270 (2015)","journal-title":"J. New Music Res."},{"key":"25_CR48","unstructured":"Miller, R.I.M.: Unity and contrast: A study of Ludwig van Beethoven\u2019s use of variation form in his symphonies, string quartets and piano sonatas. University of Glasgow (United Kingdom) (2003)"},{"key":"25_CR49","doi-asserted-by":"crossref","unstructured":"M\u00fcller, M.: Music Structure Analysis, pp. 167\u2013236. Springer International Publishing, Cham (2015)","DOI":"10.1007\/978-3-319-21945-5_4"},{"issue":"1","key":"25_CR50","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1525\/mp.2004.22.1.41","volume":"22","author":"AC North","year":"2004","unstructured":"North, A.C., Hargreaves, D.J., Hargreaves, J.J.: Uses of music in everyday life. Music. Percept. 22(1), 41\u201377 (2004)","journal-title":"Music. Percept."},{"key":"25_CR51","unstructured":"Panda, R., Malheiro, R.M., Paiva, R.P.: Audio features for music emotion recognition: a survey. IEEE Trans. Affective Comput, 99, 1\u20131 (2020)"},{"key":"25_CR52","volume-title":"The variation technique in selected piano works of Haydn, Mozart, Beethoven and Schubert: A performance project","author":"TH Pang","year":"1998","unstructured":"Pang, T.H.: The variation technique in selected piano works of Haydn, Mozart, Beethoven and Schubert: A performance project. University of Maryland, College Park (1998)"},{"key":"25_CR53","unstructured":"Paulus, J., M\u00fcller, M., Klapuri, A.: Audio-based music structure analysis. In: Proceedings of the 11th International Society for Music Information Retrieval Conference, ISMIR 2010, pp. 625\u2013636 (01 2010)"},{"key":"25_CR54","doi-asserted-by":"crossref","unstructured":"Pereira, R.M., Costa, Y.M., Aguiar, R.L., Britto, A.S., Oliveira, L.E., Silla, C.N.: Representation learning vs. handcrafted features for music genre classification. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2019)","DOI":"10.1109\/IJCNN.2019.8852334"},{"key":"25_CR55","doi-asserted-by":"crossref","unstructured":"Pipalia, K., Bhadja, R., Shukla, M.: Comparative analysis of different transformer based architectures used in sentiment analysis. In: 2020 9th International Conference System Modeling and Advancement in Research Trends (SMART), pp. 411\u2013415. IEEE (2020)","DOI":"10.1109\/SMART50582.2020.9337081"},{"key":"25_CR56","unstructured":"Prout, E.: Fugue. Library Reprints (1891)"},{"issue":"3","key":"25_CR57","doi-asserted-by":"publisher","first-page":"159","DOI":"10.2307\/829717","volume":"2","author":"L Ratner","year":"1949","unstructured":"Ratner, L.: Harmonic aspects of classic form. J. Am. Musicol. Soc. 2(3), 159\u2013168 (1949)","journal-title":"J. Am. Musicol. Soc."},{"key":"25_CR58","doi-asserted-by":"publisher","first-page":"24119","DOI":"10.1007\/s11042-020-09126-8","volume":"79","author":"S Roy","year":"2020","unstructured":"Roy, S., Biswas, M., De, D.: imusic: a session-sensitive clustered classical music recommender system using contextual representation learning. Multimedia Tools Appl. 79, 24119\u201324155 (2020)","journal-title":"Multimedia Tools Appl."},{"issue":"4","key":"25_CR59","doi-asserted-by":"publisher","first-page":"553","DOI":"10.2307\/763535","volume":"8","author":"DA Sheldon","year":"1990","unstructured":"Sheldon, D.A.: The stretto principle: some thoughts on fugue as form. J. Musicol. 8(4), 553\u2013568 (1990)","journal-title":"J. Musicol."},{"key":"25_CR60","doi-asserted-by":"publisher","DOI":"10.1016\/j.bandl.2020.104811","volume":"206","author":"ER Shi","year":"2020","unstructured":"Shi, E.R., Zhang, Q.: A domain-general perspective on the role of the basal ganglia in language and music: Benefits of music therapy for the treatment of aphasia. Brain Lang. 206, 104811 (2020)","journal-title":"Brain Lang."},{"key":"25_CR61","doi-asserted-by":"publisher","DOI":"10.3366\/edinburgh\/9780748637874.001.0001","volume-title":"Virginia Woolf and Classical Music: Politics, Aesthetics","author":"E Sutton","year":"2013","unstructured":"Sutton, E.: Virginia Woolf and Classical Music: Politics, Aesthetics. Edinburgh University Press, Form (2013)"},{"key":"25_CR62","doi-asserted-by":"publisher","unstructured":"Tavares, J.M.R., Jorge, R.M.N., et al.: Topics in Medical Image Processing and Computational Vision. Springer (2013). https:\/\/doi.org\/10.1007\/978-94-007-0726-9","DOI":"10.1007\/978-94-007-0726-9"},{"key":"25_CR63","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107917","volume":"113","author":"S Umer","year":"2021","unstructured":"Umer, S., Mondal, R., Pandey, H.M., Rout, R.K.: Deep features based convolutional neural network model for text and non-text region segmentation from document images. Appl. Soft Comput. 113, 107917 (2021)","journal-title":"Appl. Soft Comput."},{"key":"25_CR64","doi-asserted-by":"publisher","unstructured":"Verma, P.K., Agrawal, P., Madaan, V., Prodan, R.: Mcred: multi-modal message credibility for fake news detection using bert and cnn. Journal of Ambient Intelligence and Humanized Computing, pp. 1\u201313 (2022). DOI: https:\/\/doi.org\/10.1007\/s12652-022-04338-2","DOI":"10.1007\/s12652-022-04338-2"},{"key":"25_CR65","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: Internimage: Exploring large-scale vision foundation models with deformable convolutions. arXiv preprint arXiv:2211.05778 (2022)","DOI":"10.1109\/CVPR52729.2023.01385"},{"key":"25_CR66","doi-asserted-by":"crossref","unstructured":"Webster, J.: Schubert\u2019s sonata form and brahms\u2019s first maturity. Nineteenth-Century Music, pp. 18\u201335 (1978)","DOI":"10.2307\/746189"},{"key":"25_CR67","doi-asserted-by":"crossref","unstructured":"Wen, R., Chen, K., Xu, K., Zhang, Y., Wu, J.: Music main melody extraction by an interval pattern recognition algorithm. In: 2019 Chinese Control Conference (CCC), pp. 7728\u20137733. IEEE (2019)","DOI":"10.23919\/ChiCC.2019.8865954"},{"key":"25_CR68","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2020.103303","volume":"286","author":"J Wu","year":"2020","unstructured":"Wu, J., Liu, X., Hu, X., Zhu, J.: Popmnet: generating structured pop music melodies using neural networks. Artif. Intell. 286, 103303 (2020)","journal-title":"Artif. Intell."},{"key":"25_CR69","doi-asserted-by":"publisher","unstructured":"Wu, X., Lv, S., Zang, L., Han, J., Hu, S.: Conditional BERT contextual augmentation. In: Rodrigues, J.M.F., et al. (eds.) ICCS 2019. LNCS, vol. 11539, pp. 84\u201395. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-22747-0_7","DOI":"10.1007\/978-3-030-22747-0_7"},{"issue":"4","key":"25_CR70","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1017\/S0031819116000334","volume":"91","author":"JO Young","year":"2016","unstructured":"Young, J.O.: How classical music is better than popular music. Philosophy 91(4), 523\u2013540 (2016)","journal-title":"Philosophy"},{"key":"25_CR71","doi-asserted-by":"crossref","unstructured":"Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International Conference on Document Analysis and Recognition (ICDAR), pp. 1015\u20131022. IEEE (2019)","DOI":"10.1109\/ICDAR.2019.00166"}],"container-title":["Lecture Notes in Computer Science","Computational Science and Its Applications \u2013 ICCSA 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36805-9_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T10:32:15Z","timestamp":1729679535000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36805-9_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031368042","9783031368059"],"references-count":71,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36805-9_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCSA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science and Its Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccsa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccsa.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Custom based on Cyberchair 4","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"283","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":"67","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":"13","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":"24% - 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":"2.5","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":"8,5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"PHD Showcase Papers: 6(for main conf) \/ For ICCSA 2023 Workshops 876 subm sent, 350 full papers and 29 short papers accepted, additional PHD Showcase Papers: 2","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)"}}]}}