Machine learning research that matters for music creation: A case study

Journal of New Music Research

Volumn 48, Issue 1, 2019, Pages 36-55

  Sturm, Bob L ^a   Ben Tal, Oded ^b   Monaghan, Úna ^c   Collins, Nick ^d   Herremans, Dorien ^e   Chew, Elaine ^f   Hadjeres, Gaëtan ^g   Deruty, Emmanuel ^g   Pachet, François ^h  

^a ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

^b KINGSTON UNIVERSITY   (United Kingdom)

^c NEWNHAM COLLEGE   (United Kingdom)

^d DURHAM UNIVERSITY   (United Kingdom)

^e SINGAPORE UNIVERSITY OF TECHNOLOGY AND DESIGN   (Singapore)

^f QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

^g SONY COMPUTER SCIENCE LABORATORY   (France)

^h SPOTIFY   (United Kingdom)

Author keywords

Applied machine learning; computational creativity; folk music; music generation

Indexed keywords

EID: 85053055911     PISSN: 09298215     EISSN: 17445027     Source Type: Journal    
DOI: 10.1080/09298215.2018.1515233     Document Type: Article

References (50)

1. Ariza, C., (2005). Navigating the landscape of computer aided algorithmic composition systems: A definition, seven descriptors, and a lexicon of systems and research. In Proc. int. computer music conf., Barcelona, Spain
2. Begleiter, R., El-Yaniv, R., & Yona, G., (2004). On prediction using variable order markov models. Journal of Artificial Intelligence Research, 22, 385–421. doi: 10.1613/jair.1491
3. Biles, J. A., (1999). Life with GenJam: Interacting with a musical IGA. In Int. conf systems, man, cybernetics, Tokyo, Japan (Vol. 3, pp. 652–656)
4. Boulanger-Lewandowski, N., Bengio, Y., & Vincent, P., (2012). Modeling temporal dependencies in high-dimensional sequences: Application to polyphonic music generation and transcription. In Proc. int. conf. machine learn, Edinburgh, Scotland. (pp. 1159–1166)
5. Bradley, H., (1999). The companion to irish traditional music (p. 75). Cork University Press. chapter Coleman, Michael
6. Briot, J.-P., Hadjeres, G., & Pachet, F., (2017). Deep learning techniques for music generation–a survey. arXiv:1709.01620v1
7. Chew, E., (2014). Mathematical and computational modeling of tonality: Theory and applications. New York: Springer
8. Clarke, E., (1999). Rhythm and timing in music. In The psychology of music (2nd ed. pp. 473–500). San Diego, CA: Academic Press
9. Collins, N., (2009). Musical form and algorithmic composition. Contemporary Music Review, 28(1), 103–114. doi: 10.1080/07494460802664064
10. Collins, N., (2016). A funny thing happened on the way to the formula: Algorithmic composition for musical theatre. Computer Music Journal, 40(3), 41–57. doi: 10.1162/COMJ_a_00373
11. Collins, T., & Laney, R., (2017). Computer-generated stylistic compositions with long-term repetitive and phrasal structure. Journal of Creative Music Systems, 1(2). http://jcms.org.uk/issues/Vol1Issue2/computer-generated-stylistic-compositions/computer-generated-stylistic-compositions.html doi: 10.5920/JCMS.2017.02
12. Colton, S., Llano, M. T., Hepworth, R., Charnley, J., Gale, C. V., Baron, A., … Lloyd, J. R., (2016). The beyond the fence musical and computer says show documentary. In Proc. int. conf. comp. creativity, Paris, France
13. Cope, D., (1991). Computers and musical style. Oxford, UK: Oxford University Press
14. Dannenberg, R. B., Thom, B., & Watson, D., (1997). A machine learning approach to musical style recognition. In Proc. int. computer music conf., Thessaloniki, Greece (pp. 344–347)
15. Dean, R., (Ed.). (2018). Oxford handbook of algorithmic composition. Oxford, UK: Oxford University Press
16. Drummond, C., & Japkowicz, N., (2010). Warning: Statistical benchmarking is addictive. Kicking the habit in machine learning. Journal of Experimental & Theoretical Artificial Intelligence, 22, 67–80. doi: 10.1080/09528130903010295
17. Fernández, J. D., & Vico, F., (2013). AI methods in algorithmic composition: A comprehensive survey. Journal of Artificial Intelligence Research, 48(1), 513–582. doi: 10.1613/jair.3908
18. Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R., & Schmidhuber, J., (2016). LSTM: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems, 28(10), 2222–2232. doi: 10.1109/TNNLS.2016.2582924
19. Hadjeres, G., Pachet, F., & Nielsen, F., (2017). DeepBach: A steerable model for Bach chorales generation. In Proc. int. conf. machine learning, Sydney, Australia (pp. 1362–1371)
20. Hansen, P., Mladenović, N., & Perez-Britos, D., (2001). Variable neighborhood decomposition search. Journal of Heuristics, 7(4), 335–350. doi: 10.1023/A:1011336210885
21. Herremans, D., & Chew, E., (2016a). Morpheus: Automatic music generation with recurrent pattern constraints and tension proles. In Proc. IEEE TENCON, Marina Bay Sands, Singapore
22. Herremans, D., & Chew, E., (2016b). Tension ribbons: Quantifying and visualising tonal tension. In Int. conf. technologies for music notation and representation (TENOR), Cambridge, UK (pp. 8–18)
23. Herremans, D., & Chew, E., (2018). Morpheus: Generating structured music with constrained patterns and tension. IEEE Transactions on Affective Computing. https://ieeexplore.ieee.org/document/8007229/
24. Herremans, D., Chuan, C.-H., & Chew, E., (2017). A functional taxonomy of music generation systems. ACM Computing Surveys, 50(5), 69:1–69:30. doi: 10.1145/3108242
25. Herremans, D., Weisser, S., Sörensen, K., & Conklin, D., (2015). Generating structured music for bagana using quality metrics based on markov models. Expert Systems with Applications, 42(21), 7424–7435. doi: 10.1016/j.eswa.2015.05.043
26. Hild, H., Feulner, J., & Menzel, W., (1992). HARMONET: A neural net for harmonizing chorales in the style of J. S. Bach. In Neural information processing systems, Denver, Colorado (pp. 267–274)
27. Hiller, L., & Isaacson, L., (1959). Experimental music: Composition with an electronic computer. New York: McGraw-Hill Book Company, Inc
28. Holzapfel, A., Sturm, B. L., & Coeckelbergh, M., (2018). Ethical dimensions of music information retrieval technology. Transactions of the International Society for Music Information Retrieval. (in press)
29. Jaques, N., Gu, S., Turner, R. E., & Eck, D., (2016). Generating music by fine-tuning recurrent neural networks with reinforcement learning. In Deep reinforcement learning workshop, NIPS, Barcelona, Spain
30. Jaques, N., Gu, S., Turner, R. E., & Eck, D., (2017). Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control. In Proc. int. conf. machine learning, Sydney, Australia
31. Jordanous, A., (2017). Has computational creativity successfully made it “Beyond the Fence” in musical theatre? Connection Science, 29(4), 350–386. doi: 10.1080/09540091.2017.1345857
32. Kaul, A., (2009). Turning the tune: Traditional music, tourism, and social change in an Irish village. New York: Berghahn Books
33. Mauch, M., & Dixon, S., (2010). Approximate note transcription for the improved identification of difficult chords. In Proc. int. soc. music info. retrieval, Utrecht, Netherlands
34. Meredith, D., (2015). Music analysis and point-set compression. Journal of New Music Research, 44(3), 245–270. doi: 10.1080/09298215.2015.1045003
35. Meredith, D., Wiggins, G. A., & Lemström, K., (2002). Method of pattern discovery. PCT patent application number PCT/GB02/02430, UK patent application, (0211914.7)
36. Miranda, E., (Ed.). (2000). Readings in music and artificial intelligence. London, UK: Routledge
37. Miranda, E., & Biles, J. A., (2007). Evolutionary computer music. London: Springer-Verlag
38. Nierhaus, G., (2008). Algorithmic composition: Paradigms of automated music generation. Vienna, Austria: Springer
39. O'Shea, H., (2008). The making of Irish traditional music. Cork, Ireland: Cork University Press
40. Pearce, M., Meredith, D., & Wiggins, G., (2002). Motivations and methodologies for automation of the compositional process. Musicae Scientiae, 6(2), 119–147. doi: 10.1177/102986490200600203
41. Roads, C., (1996). Computer music tutorial. Cambridge, MA: The MIT Press
42. Salamon, J., & Gómez, E., (2012). Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Transactions on Audio, Speech, and Language Processing, 20(6), 1759–1770. doi: 10.1109/TASL.2012.2188515
43. Schedl, M., Gomez, E., & Urbano, J., (2014). Music information retrieval: Recent developments and applications. Foundations and Trends in Information Retrieval, 8(2–3), 127–261. doi: 10.1561/1500000042
44. Sturm, B. L., (2014). A simple method to determine if a music information retrieval system is a “horse”. IEEE Transactions on Multimedia, 16(6), 1636–1644. doi: 10.1109/TMM.2014.2330697
45. Sturm, B. L., (2018). What do these 5,599,881 parameters mean? an analysis of a specific lstm music transcription model, starting with the 70,281 parameters of its softmax layer. In Proc. music metacreation workshop of ICCC, Salamanca, Spain
46. Sturm, B. L., & Ben-Tal, O., (2017). Taking the models back to music practice: Evaluating generative transcription models built using deep learning. Journal of Creative Music Systems, 2(1), 1–29. doi: 10.5920/JCMS.2017.09
47. Sturm, B. L., Santos, J. F., Ben-Tal, O., & Korshunova, I., (2016). Music transcription modelling and composition using deep learning. In Proc. conf. computer simulation of musical creativity, Huddersfield, UK
48. Todd, P. M., & Loy, G. D., (1991). Music and connectionism. Cambridge, MA: The MIT Press
49. Vallely, F., Hamilton, C., Vallely, E., & Doherty, L., (Eds.). (1999). Crosbhealach an Cheoil: The crossroads conference 1996: Tradition and change in Irish traditional music. Whinstone Music
50. Wagstaff, K. L., (2012). Machine learning that matters. In Proc. int. conf. machine learning, Edinburgh, Scotland (pp. 529–536)