{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T17:11:52Z","timestamp":1743095512812,"version":"3.40.3"},"publisher-location":"Cham","reference-count":10,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031376481"},{"type":"electronic","value":"9783031376498"}],"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:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T00:00:00Z","timestamp":1690243200000},"content-version":"vor","delay-in-days":205,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The intricate temporally prolonged sequences seen in music make it a perfect environment for the study of prediction. Melody, harmony, and rhythm are three examples of the structural elements found in music. This research incorporates music excerpts prediction by understanding structural details using Markov chain and LSTM models. The novel approach compares to state-of-the-art algorithms by predicting how a musical excerpt would continue after being given as input. To compare the variations in prediction and learning, different learning models with different input feature representations were utilized. This algorithm envisions multitude of usage including next generation music recommendation system using intra-sequence matching, pitch-tone correction, amongst others by integrating with recent advances in deep learning, computer vision, and speech techniques.<\/jats:p>","DOI":"10.1007\/978-3-031-37649-8_3","type":"book-chapter","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T04:02:08Z","timestamp":1690257728000},"page":"26-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Music Using Machine Learning"],"prefix":"10.1007","author":[{"given":"Aishwarya","family":"Asesh","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-70163-9","volume-title":"Deep Learning Techniques for Music Generation","author":"J-P Briot","year":"2020","unstructured":"Briot, J.-P., Hadjeres, G., Pachet, F.-D.: Deep Learning Techniques for Music Generation, vol. 1. Springer, Heidelberg (2020)"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Lisena, P., Mero\u00f1o-Pe\u00f1uela, A., Troncy, R.: MIDI2vec: learning MIDI embeddings for reliable prediction of symbolic music metadata. Semant. Web Preprint 13, 1-21 (2022)","DOI":"10.3233\/SW-210446"},{"key":"3_CR3","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1007\/978-981-16-5157-1_3","volume-title":"Sentimental Analysis and Deep Learning","author":"A Asesh","year":"2022","unstructured":"Asesh, A.: SentiSeries: a trilogy of customer reviews, sentiment analysis and time series. In: Shakya, S., Balas, V.E., Kamolphiwong, S., Du, K.-L. (eds.) Sentimental Analysis and Deep Learning. AISC, vol. 1408, pp. 31\u201345. Springer, Singapore (2022). https:\/\/doi.org\/10.1007\/978-981-16-5157-1_3"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Kim, S.T., Oh, J.H.: Music intelligence: granular data and prediction of top ten hit songs. Decis. Support Syst. 145, 113535 (2021)","DOI":"10.1016\/j.dss.2021.113535"},{"issue":"57","key":"3_CR5","first-page":"1","volume":"22","author":"G Papamakarios","year":"2021","unstructured":"Papamakarios, G., Nalisnick, E., Rezende, D.J., Mohamed, S., Lakshminarayanan, B.: Normalizing flows for probabilistic modeling and inference. J. Mach. Learn. Res. 22(57), 1\u201364 (2021)","journal-title":"J. Mach. Learn. Res."},{"key":"3_CR6","doi-asserted-by":"publisher","first-page":"e59","DOI":"10.1017\/S0140525X20000333","volume":"44","author":"PE Savage","year":"2021","unstructured":"Savage, P.E.: Music as a coevolved system for social bonding. Behav. Brain Sci. 44, e59 (2021)","journal-title":"Behav. Brain Sci."},{"issue":"2","key":"3_CR7","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1007\/s10462-021-09996-w","volume":"55","author":"S Gronauer","year":"2022","unstructured":"Gronauer, S., Diepold, K.: Multi-agent deep reinforcement learning: a survey. Artif. Intell. Rev. 55(2), 895\u2013943 (2022)","journal-title":"Artif. Intell. Rev."},{"key":"3_CR8","first-page":"21923","volume":"34","author":"C Guzm\u00e1n","year":"2021","unstructured":"Guzm\u00e1n, C., Mehta, N., Mortazavi, A.: Best-case bounds in online learning. Adv. Neural Inf. Syst. 34, 21923\u201321934 (2021)","journal-title":"Adv. Neural Inf. Syst."},{"key":"3_CR9","first-page":"15084","volume":"34","author":"L Chen","year":"2021","unstructured":"Chen, L., et al.: Decision transformer: reinforcement learning via sequence modeling. Adv. Neural. Inf. Process. Syst. 34, 15084\u201315097 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"3_CR10","first-page":"27580","volume":"34","author":"J Schrittwieser","year":"2021","unstructured":"Schrittwieser, J., Hubert, T., Mandhane, A., Barekatain, M., Antonoglou, I., Silver, D.: Online and offline reinforcement learning by planning with a learned model. Adv. Neural. Inf. Process. Syst. 34, 27580\u201327591 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."}],"container-title":["Lecture Notes in Networks and Systems","Digital Interaction and Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-37649-8_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T04:10:22Z","timestamp":1690258222000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-37649-8_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031376481","9783031376498"],"references-count":10,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-37649-8_3","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"25 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"MIDI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Machine Intelligence and Digital Interaction Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"midi12022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/midi2022.opi.org.pl\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}