References
- 1Aebersold, J. (1967). A New Approach to Jazz Improvisation.
- 2Ake, D. A. (2002). Jazz Cultures. University of California Press, Berkeley. DOI: 10.1525/9780520926967
- 3Arcos, J. L., Lopéz de Mántaras, R., and Serra, X. (1998). SaxEx: A case-based reasoning system for generating expressive musical performances. Journal of New Music Research, 27:194–210. DOI: 10.1080/09298219808570746
- 4Biles, J. A. (1994). GenJam: Evolution of a jazz improviser. In Proceedings of the International Computer Music Conference (ICMC), pages 131–137.
- 5Chamberlain, R., Mullin, C., Scheerlinck, B., and Wagemans, J. (2018). Putting the art in artificial: Aesthetic responses to computer-generated art. Psychology of Aesthetics, Creativity, and the Arts, 12(2):177–192. DOI: 10.1037/aca0000136
- 6Coker, J. (1987). Improvising Jazz. Simon & Schuster, New York, 1st fireside edition.
- 7Cooke, M., and Horn, D. (2002). The Cambridge Companion to Jazz. Cambridge University Press, Cambridge, UK. OCLC: 758544526. DOI: 10.1017/CCOL9780521663205
- 8Friberg, A., Gulz, T., and Wettebrandt, C. (2021). Computer tools for modeling swing timing interactions in a jazz ensemble. In 16th International Conference on Music Perception and Cognition and 11th Triennial Conference of the European Society for the Cognitive Sciences of Music (ICMPC16-ESCOM11), Sheffield, UK.
- 9Frieler, K. (2018).
A feature history of jazz solo improvisation . In Knauer, W., editor, Jazz @ 100: An Alternative to a Story of Heroes, volume 15 of Darmstadt Studies in Jazz Research. Wolke Verlag, Hofheim am Taunus. - 10Frieler, K. (2019).
Constructing jazz lines: Taxonomy, vocabulary, grammar . In Pfleiderer, M. and Zaddach, W.-G., editors, Jazzforschung heute: Themen, Methoden, Perspektiven, Berlin. Edition Emwas. - 11Frieler, K. (2020). Miles vs. Trane: Computational and statistical comparison of the improvisatory styles of Miles Davis and John Coltrane. Jazz Perspectives, 12(1):123–145. DOI: 10.1080/17494060.2020.1734053
- 12Frieler, K., Pfleiderer, M., Abeßer, J., and Zaddach, W.-G. (2016). Midlevel analysis of monophonic jazz solos: A new approach to the study of improvisation. Musicae Scientiae, 20(2):143–162. DOI: 10.1177/1029864916636440
- 13Grachten, M. (2001).
JIG: Jazz Improvisation Generator . In Proceedings of the MOSART Workshop on Current Research Directions in Computer Music, Barcelona, Spain. - 14Haviv Hakimi, S., Bhonker, N., and El-Yaniv, R. (2020). BebopNet: Deep neural models for personalized jazz improvisations. In Proceedings of the 21st International Society for Music Information Retrieval Conference, Montréal, Canada.
- 15Hiller, L. A., and Isaacson, L. M. (1959). Experimental Music: Composition With an Electronic Computer. McGraw-Hill, New York.
- 16Hung, H.-T., Wang, C.-Y., Yang, Y.-H., and Wang, H.-M. (2019). Improving automatic jazz melody generation by transfer learning techniques. In Proceedings of the Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pages 1–8, Lanzhou, China. DOI: 10.1109/APSIPAASC47483.2019.9023224
- 17Johnson-Laird, P. N. (1991).
Jazz improvisation: A theory at the computational level . In Howell, P., West, R., and Cross, I., editors, Representing Musical Structure, London. Academic Press. - 18Johnson-Laird, P. N. (2002). How jazz musicians improvise. Music Perception, 10:415–442. DOI: 10.1525/mp.2002.19.3.415
- 19Kaufman, J. C., and Beghetto, R. A. (2009). Beyond big and little: The Four C Model of Creativity. Review of General Psychology, 13(1):1–12. DOI: 10.1037/a0013688
- 20Keller, R., Schofield, A., Toman-Yih, A., Merritt, Z., and Elliott, J. (2013). Automating the explanation of jazz chord progressions using idiomatic analysis. Computer Music Journal, 37(4):54–69. DOI: 10.1162/COMJ_a_00201
- 21Keller, R. M., and Morrison, D. (2007). A grammatical approach to automatic improvisation. In Proceedings of the 4th Sound and Music Computing Conference, pages 330–337, Lefkada, Greece.
- 22Lothwesen, K., and Frieler, K. (2012).
Gestaltungsmuster und Ideenuss in Jazzpiano-Improvisationen: Eine Pilotstudie zum Einfluss von Tempo, Tonalität und Expertise . In Lehmann, A., Jeßulat, A., and Wünsch, C., editors, Kreativität: Struktur und Emotion. Königshausen & Neumann, Würzburg. - 23Madaghiele, V., Lisena, P., and Troncy, R. (2021). MINGUS: Melodic improvisation neural generator using Seq2Seq. In 22nd International Society for Music Information Retrieval Conference.
- 24Moffat, D., and Kelly, M. (2006). An investigation into people’s bias against computational creativity in music composition. In Third Joint Workshop on Computational Creativity, ECAI 2006, Trento, Italy.
Universita di Trento . - 25Narmour, E. (1990). The Analysis and Cognition of Basic Melodic Structures: The Implication-Realization Model. University of Chicago Press, Chicago.
- 26Norgaard, M. (2011). Descriptions of improvisational thinking by artist-level jazz musicians. Journal of Research in Music Education, 59(2):109–127. DOI: 10.1177/0022429411405669
- 27Norgaard, M. (2014). How jazz musicians improvise: The central role of auditory and motor patterns. Music Perception: An Interdisciplinary Journal, 31(3):271–287. DOI: 10.1525/mp.2014.31.3.271
- 28Owens, T. (1974). Charlie Parker: Techniques of Improvisation. PhD thesis, University of California, Los Angeles.
- 29Pachet, F. (2003). The Continuator: Musical interaction with style. Journal of New Music Research, 32(3):333–341. DOI: 10.1076/jnmr.32.3.333.16861
- 30Pachet, F. (2012).
Musical virtuosity and creativity . In McCormack, J. and d’Inverno, M., editors, Computers and Creativity, pages 115–146. Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-31727-9_5 - 31Papadopoulos, G., and Wiggins, G. (1998). A genetic algorithm for the generation of jazz melodies. In Human and Artificial Information Processing: Finnish Conference on Artificial Intelligence (STeP’98), Jyväskylä, Finland.
- 32Pfleiderer, M., Frieler, K., Abeßer, J., Zaddach, W.-G., and Burkhart, B., editors (2017). Inside the Jazzomat: New Perspectives for Jazz Research. Schott Music GmbH & Co. KG, Mainz, 1. Auflage OCLC: 1015349144.
- 33Pressing, J. (1984). Cognitive Processes in Improvisation. Cognitive Processes in the Perception of Art. Elsevier, North-Holland. DOI: 10.1016/S0166-4115(08)62358-4
- 34Pressing, J. (1988).
Improvisation: Method and models . In Sloboda, J. A., editor, Generative Processes in Music: The Psychology of Performance, Improvisation, and Composition, pages 129–178, Oxford. Clarendon. DOI: 10.1093/acprof:oso/9780198508465.003.0007 - 35Quick, D., and Thomas, K. (2019). A functional model of jazz improvisation. In Proceedings of the 7th ACM SIGPLAN International Workshop on Functional Art, Music, Modeling, and Design (FARM 2019), pages 11–21, Berlin, Germany.
ACM Press . DOI: 10.1145/3331543.3342577 - 36Ramalho, G. L., Rolland, P.-Y., and Ganascia, J.-G. (1999). An artificially intelligent jazz performer. Journal of New Music Research, 28(2):105–129. DOI: 10.1076/jnmr.28.2.105.3120
- 37Russell, G. (1953). George Russell’s Lydian chromatic concept of tonal organization. Concept Pub. Co, Brookline, Mass. OCLC: ocm50075662.
- 38Schütz, M. (2015). Improvisation im Jazz: eine empirische Untersuchung bei Jazzpianisten auf der Basis der Ideenussanalyse. Number 34 in Schriftenreihe Studien zur Musikwissenschaft. Kovač, Hamburg. OCLC: 915812622.
- 39Toiviainen, P. (1995). Modelling the target-note technique of bebop-style jazz improvisation: An artificial neural network approach. Music Perception, 12:398–413. DOI: 10.2307/40285674
- 40Trieu, N., and Keller, R. (2018). JazzGAN: Improvising with generative adversarial networks. In Proceedings of the 6th International Workshop on Musical Metacreation (MUME 2018), Salamanca, Spain.
- 41Von Hippel, P., and Huron, D. (2000). Why do skips precede reversals? The effect of tessitura on melodic structure. Music Perception: An Interdisciplinary Journal, 18(1):59–85. DOI: 10.2307/40285901
- 42Wiggins, G., and Pearce, M. (2001). Towards a framework for the evaluation of machine compositions. In Proceedings of the AISB’01 Symposium on Artificial Intelligence and Creativity in the Arts and Sciences, pages 22–32, York.
- 43Wu, S.-L., and Yang, Y.-H. (2020). The Jazz Transformer on the front line: Exploring the shortcomings of AI-composed music through quantitative measures. In 21st International Society for Music Information Retrieval Conference, pages 142–149, Montréal, Canada.
- 44Yang, L.-C., and Lerch, A. (2020). On the evaluation of generative models in music. Neural Computing and Applications, 32(9):4773–4784. DOI: 10.1007/s00521-018-3849-7