Learning expressive performance: The structure‐level approach

References

• Aha , D.W. , Kibler , D. and Albert , M. 1991 . Instance‐based learning algorithms . Machine Learning , 6 (1) : 37 – 66 .
   Web of Science ®Google Scholar
• Bresin , R. and Vecchio , C. Analysis and synthesis of the performing action of a real pianist by means of artificial neural networks . Proceedings of the Third International Conference on Music Perception and Cognition . University of Liège .
   Google Scholar
• Clarke , E. 1988 . “ Generative principles in music performance ” . In Generative Processes in Music , Edited by: Sloboda , J. Oxford : Clarendon Press .
   Google Scholar
• Desain , P. and Honing , H. Tempo curves considered harmful. A critical review of the representation of timing in computer music . Proceedings of the International Computer Music Conference 1991 . San Francisco, CA. International Computer Music Association .
   Google Scholar
• Desain , P. and Honing , H. 1992 . Time functions function best as functions of multiple times . Computer Music Journal , 16 (2) : 17 – 34 .
   Web of Science ®Google Scholar
• Friberg , A. 1995 . A Quantitative Rule System for Musical Performance , Stockholm : Royal Institute of Technology (KTH) . Ph.D. dissertation,
   Google Scholar
• Honing , H. POCO: An environment for analysing, modifying, and generating expression in music . Proceedings of the International Computer Music Conference 1990 . San Francisco, CA. International Computer Music Association .
   Google Scholar
• Katayose , H. , Fukuoka , T. , Takami , K. and Inokuchi , S. Expression extraction in virtuoso music performances . Proceedings of the Tenth International Conference on Pattern Recognition . Atlantic City, N.J..
   Google Scholar
• Lerdahl , F. and Jackendoff , R. 1983 . A Generative Theory of Tonal Music , Cambridge, MA : MIT Press .
   Google Scholar
• Mazzola , G. and Zahorka , O. 1994 . Tempo curves revisited ‐ hierarchies of performance fields . Computer Music Journal , 18 (1) : 40 – 52 .
   Web of Science ®Google Scholar
• Narmour , E. 1977 . Beyond Schenkerism , Chicago : University of Chicago Press .
   Google Scholar
• Quinlan , J.R. 1990 . Learning logical definitions from relations . Machine Learning , 5 (3) : 239 – 266 .
   Web of Science ®Google Scholar
• Repp , B. 1992 . Diversity and commonality in music performance: An analysis of timing microstructure in Schumann's “Träumerei” . Journal of the Acoustical Society of America , 92 (5) : 2546 – 2568 .
   PubMed Web of Science ®Google Scholar
• Sloboda , J. 1985 . The Musical Mind: The Cognitive Psychology of Music , Oxford : Clarendon Press .
   Google Scholar
• Sundberg , J. , Askenfelt , A. and Frydén , L. 1983 . Musical performance: A synthesis‐by‐rule approach . Computer Music Journal , 7 (1) : 37 – 43 .
   Web of Science ®Google Scholar
• Sundberg , J. , Friberg , A. and Frydén , L. 1991 . “ Common secrets of musicians and listeners: An analysis‐by‐synthesis study of musical performance ” . In Representing Musical Structure , Edited by: Howell , P. , West , R. and Cross , I. London : Academic Press .
   Google Scholar
• Todd , N. 1989 . Towards a cognitive theory of expression: The performance and perception of rubato . Contemporary Music Review , 4 : 405 – 416 .
   Google Scholar
• Todd , N. 1992 . The dynamics of dynamics: A model of musical expression . Journal of the Acoustical Society of America , 91 (6) : 3540 – 3550 .
   Web of Science ®Google Scholar
• Widmer , G. 1992a . “ A knowledge‐intensive approach to machine learning in music ” . In Understanding Music with AI: Perspectives on Music Cognition , Edited by: Balaban , M. , Ebcioglu , K. and Laske , O. Menlo Park, CA : AAAI Press .
   Google Scholar
• Widmer , G. 1992b . Qualitative perception modeling and intelligent musical learning . Computer Music Journal , 16 (2) : 51 – 68 .
   Web of Science ®Google Scholar
• Widmer , G. 1993 . Combining knowledge‐based and instance‐based learning to exploit qualitative knowledge . Informatica , 17 (Special Issue on Multistrategy Learning) : 371 – 385 .
   Google Scholar
• Widmer , G. 1995a . Modelling the rational basis of musical expression . Computer Music Journal , 19 (2) : 76 – 96 .
   Web of Science ®Google Scholar
• Widmer , G. A machine learning analysis of expressive timing in pianists’ performances of Schumann's “Träumerei” . Proceedings of the Stockholm Symposium on Generative Grammars for Music Performance . Stockholm, Sweden. Edited by: Sundberg , J. Royal Institute of Technology (KTH) .
   Google Scholar