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

References

• 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.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• 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).
   Google Scholar
• 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).
   Google Scholar
• Bradley, H. (1999). The companion to irish traditional music (p. 75). Cork University Press. chapter Coleman, Michael.
   Google Scholar
• Briot, J.-P., Hadjeres, G., & Pachet, F. (2017). Deep learning techniques for music generation – a survey. arXiv:1709.01620v1.
   Google Scholar
• Chew, E. (2014). Mathematical and computational modeling of tonality: Theory and applications. New York: Springer.
   Google Scholar
• Clarke, E. (1999). Rhythm and timing in music. In The psychology of music (2nd ed. pp. 473–500). San Diego, CA: Academic Press.
   Google Scholar
• Collins, N. (2009). Musical form and algorithmic composition. Contemporary Music Review, 28(1), 103–114. doi: 10.1080/07494460802664064
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• 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
   Google Scholar
• 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.
   Google Scholar
• Cope, D. (1991). Computers and musical style. Oxford, UK: Oxford University Press.
   Google Scholar
• 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).
   Google Scholar
• Dean, R. (Ed.). (2018). Oxford handbook of algorithmic composition. Oxford, UK: Oxford University Press.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• 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
   Google Scholar
• 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
   PubMed Web of Science ®Google Scholar
• 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).
   Google Scholar
• Hansen, P., Mladenović, N., & Perez-Britos, D. (2001). Variable neighborhood decomposition search. Journal of Heuristics, 7(4), 335–350. doi: 10.1023/A:1011336210885
   Web of Science ®Google Scholar
• Herremans, D., & Chew, E. (2016a). Morpheus: Automatic music generation with recurrent pattern constraints and tension proles. In Proc. IEEE TENCON, Marina Bay Sands, Singapore.
   Google Scholar
• 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).
   Google Scholar
• 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/
   Google Scholar
• 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
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• 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).
   Google Scholar
• Hiller, L., & Isaacson, L. (1959). Experimental music: Composition with an electronic computer. New York: McGraw-Hill Book Company, Inc.
   Google Scholar
• 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).
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Kaul, A. (2009). Turning the tune: Traditional music, tourism, and social change in an Irish village. New York: Berghahn Books.
   Google Scholar
• Mauch, M., & Dixon, S. (2010). Approximate note transcription for the improved identification of difficult chords. In Proc. int. soc. music info. retrieval, Utrecht, Netherlands.
   Google Scholar
• Meredith, D. (2015). Music analysis and point-set compression. Journal of New Music Research, 44(3), 245–270. doi: 10.1080/09298215.2015.1045003
   Web of Science ®Google Scholar
• Meredith, D., Wiggins, G. A., & Lemström, K. (2002). Method of pattern discovery. PCT patent application number PCT/GB02/02430, UK patent application, (0211914.7).
   Google Scholar
• Miranda, E. (Ed.). (2000). Readings in music and artificial intelligence. London, UK: Routledge.
   Google Scholar
• Miranda, E., & Biles, J. A. (2007). Evolutionary computer music. London: Springer-Verlag.
   Google Scholar
• Nierhaus, G. (2008). Algorithmic composition: Paradigms of automated music generation. Vienna, Austria: Springer.
   Google Scholar
• O'Shea, H. (2008). The making of Irish traditional music. Cork, Ireland: Cork University Press.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• Roads, C. (1996). Computer music tutorial. Cambridge, MA: The MIT Press.
   Google Scholar
• 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
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• 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.
   Google Scholar
• 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
   Google Scholar
• 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.
   Google Scholar
• Todd, P. M., & Loy, G. D. (1991). Music and connectionism. Cambridge, MA: The MIT Press.
   Google Scholar
• 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.
   Google Scholar
• Wagstaff, K. L. (2012). Machine learning that matters. In Proc. int. conf. machine learning, Edinburgh, Scotland (pp. 529–536).
   Google Scholar