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In an effort to increase innovation in the selection of pitches and note durations, we present a system that discovers musical motifs by coupling machine-learning techniques with an inspirational component. Unlike many generative models, the inspirational component allows the composition process to originate outside of what is learned from the training data. Candidate motifs are extracted from non-musical data such as audio, images, and sleep signals. Machine-learning algorithms select the motifs that most resemble the training data. We find that the inspirational motif discovery process is more efficient than random generation. We also extract motifs from real music scores, identify themes in the piece according to a theme database, and measure the probability of discovering thematic motifs verses non-thematic motifs. We examine the information content of the motifs by comparing the entropy of the discovered motifs, candidate motifs, and training data. We measure innovation by comparing the probability of the training data and the probability of the discovered motifs given the model.<\/jats:p>","DOI":"10.1145\/2888403","type":"journal-article","created":{"date-parts":[[2017,1,10]],"date-time":"2017-01-10T15:41:17Z","timestamp":1484062877000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Musical Motif Discovery from Non-Musical Inspiration Sources"],"prefix":"10.1145","volume":"14","author":[{"given":"Daniel","family":"Johnson","sequence":"first","affiliation":[{"name":"Computer Science Department, Brigham Young University Provo, UT 84602 USA"}]},{"given":"Dan","family":"Ventura","sequence":"additional","affiliation":[{"name":"Computer Science Department, Brigham Young University Provo, UT 84602 USA"}]}],"member":"320","published-online":{"date-parts":[[2017,1,10]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/1622487.1622499"},{"key":"e_1_2_1_2_1","volume-title":"Proceedings of the International Computer Music Conference. 131--137","author":"Biles John","year":"1994","unstructured":"John Biles . 1994 . GenJam: A genetic algorithm for generating jazz solos . In Proceedings of the International Computer Music Conference. 131--137 . John Biles. 1994. GenJam: A genetic algorithm for generating jazz solos. In Proceedings of the International Computer Music Conference. 131--137."},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of AISB Symposium on Artificial Intelligence and Creativity in Arts and Science. 41--49","author":"Blackwell T. M.","year":"2003","unstructured":"T. M. Blackwell . 2003 . Swarm music: Improvised music with multi-swarms . In Proceedings of AISB Symposium on Artificial Intelligence and Creativity in Arts and Science. 41--49 . T. M. Blackwell. 2003. Swarm music: Improvised music with multi-swarms. In Proceedings of AISB Symposium on Artificial Intelligence and Creativity in Arts and Science. 41--49."},{"volume-title":"Images and Ideas in Modern French Piano Music: The Extra-musical Subtext in Piano Works by Ravel, Debussy, and Messiaen","author":"Bruhn Siglind","key":"e_1_2_1_4_1","unstructured":"Siglind Bruhn . 1997. Images and Ideas in Modern French Piano Music: The Extra-musical Subtext in Piano Works by Ravel, Debussy, and Messiaen . Vol. 6 . Pendragon Press . Siglind Bruhn. 1997. Images and Ideas in Modern French Piano Music: The Extra-musical Subtext in Piano Works by Ravel, Debussy, and Messiaen. Vol. 6. Pendragon Press."},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1162\/014892699560001"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v30i3.2252"},{"volume-title":"Experiments in Musical Intelligence","author":"Cope David","key":"e_1_2_1_7_1","unstructured":"David Cope . 1996. Experiments in Musical Intelligence , Vol. 12 . AR Editions , Madison, WI . David Cope. 1996. Experiments in Musical Intelligence, Vol. 12. AR Editions, Madison, WI."},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2003.1236474"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/646259.684297"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.2006.18.7.1527"},{"volume-title":"A Field Guide to Dynamical Recurrent Neural Networks","author":"Hochreiter Sepp","key":"e_1_2_1_12_1","unstructured":"Sepp Hochreiter , Yoshua Bengio , Paolo Frasconi , and J\u00fcrgen Schmidhuber . 2001. Gradient flow in recurrent nets: The difficulty of learning long-term dependencies . In A Field Guide to Dynamical Recurrent Neural Networks . IEEE Press , 237--244. Sepp Hochreiter, Yoshua Bengio, Paolo Frasconi, and J\u00fcrgen Schmidhuber. 2001. Gradient flow in recurrent nets: The difficulty of learning long-term dependencies. In A Field Guide to Dynamical Recurrent Neural Networks. IEEE Press, 237--244."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/860435.860515"},{"key":"e_1_2_1_14_1","first-page":"321","article-title":"Grammar based music composition","volume":"96","author":"McCormack Jon","year":"1996","unstructured":"Jon McCormack . 1996 . Grammar based music composition . Complex Syst. 96 (1996), 321 -- 336 . Jon McCormack. 1996. Grammar based music composition. Complex Syst. 96 (1996), 321--336.","journal-title":"Complex Syst."},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1357054.1357169"},{"volume-title":"Proceedings of 8th Artificial Intelligence and Interactive Digital Entertainment Conference. 63--67","author":"Benjamin","key":"e_1_2_1_16_1","unstructured":"Benjamin D. Smith and Guy E. Garnett. 2012. Improvising musical structure with hierarchical neural nets . In Proceedings of 8th Artificial Intelligence and Interactive Digital Entertainment Conference. 63--67 . Benjamin D. Smith and Guy E. Garnett. 2012. Improvising musical structure with hierarchical neural nets. In Proceedings of 8th Artificial Intelligence and Interactive Digital Entertainment Conference. 63--67."},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of International Conference on Computational Creativity. 160--164","author":"Smith Robert","year":"2012","unstructured":"Robert Smith , Aaron Dennis , and Dan Ventura . 2012 . Automatic composition from non-musical inspiration sources . In Proceedings of International Conference on Computational Creativity. 160--164 . Robert Smith, Aaron Dennis, and Dan Ventura. 2012. Automatic composition from non-musical inspiration sources. In Proceedings of International Conference on Computational Creativity. 160--164."},{"key":"e_1_2_1_18_1","volume-title":"A topic model for melodic sequences. ArXiv E-prints","author":"Spiliopoulou Athina","year":"2012","unstructured":"Athina Spiliopoulou and Amos Storkey . 2012. A topic model for melodic sequences. ArXiv E-prints ( 2012 ). Athina Spiliopoulou and Amos Storkey. 2012. A topic model for melodic sequences. ArXiv E-prints (2012)."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-34156-4_36"},{"volume-title":"The Analysis of Music","author":"White John David","key":"e_1_2_1_21_1","unstructured":"John David White . 1976. The Analysis of Music . Prentice-Hall . John David White. 1976. The Analysis of Music. Prentice-Hall."},{"volume-title":"Oxford Handbook of Computer Music","author":"Wiggins Gerraint A.","key":"e_1_2_1_22_1","unstructured":"Gerraint A. Wiggins , Marcus T. Pearce , and Daniel M\u00fcllensiefen . 2009. Computational modelling of music cognition and musical creativity . In Oxford Handbook of Computer Music . Oxford University Press , 383--420. Gerraint A. Wiggins, Marcus T. Pearce, and Daniel M\u00fcllensiefen. 2009. Computational modelling of music cognition and musical creativity. In Oxford Handbook of Computer Music. 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