Query Results

The following 60 items having a title containing 'music' published later than
1980 have been found in the database.

1. : Current Research in Computer-Generated Music, in Special Issue on
   Computer-Generated Music, Computer, 24(7), 54-75, 1991. 
2. : Special Issue on Computer-Generated Music, Computer, 24(7), 1991. 
3. Ames C.: Artificial Intelligence and Musical Composition, in Kurzweil R.(ed.),
   The Age of Intelligent Machines, MIT Press, Cambridge, MA, pp.386-389, 1990. 
4. Anderson D.P., Kuivila R.: Formula: A Programming Language for Expressive
   Computer Music, in Special Issue on Computer-Generated Music, Computer, 24(7),
   12-21, 1991. 
5. Barlow J.P.: Music in Cyberspace; Cyberspace as Place, in The First Conference
   on Cyberspace, Collected Abstracts, School of Architecture, University of
   Texas at Austin, p.4, 1990. 
6. Camurri A.(ed.): Special Issue on Artificial Intelligence and Music,
   Interface, 19(2-3), 1990. 
7. Camurri A., Innocenti C., Frixione M., Zaccaria R.: A Model of Representation
   and Communication of Music and Multimedia Knowledge, in Neumann B.(ed.), 
   Proceedings of the Tenth European Conference on Artificial Intelligence
   (ECAI92), Wiley, Chichester, UK, pp.164-168, 1992. 
8. Camurri A., Innocenti C., Massucco C.: Multi-Paradigm Software Environment for
   the Real-time Processing of Sound, Music, and Multimedia, Knowledge-Based
   Systems, 7(2), 1994. 
9. Cope D.: Recombinant Music: Using the Computer to Explore Musical Style, in
   Special Issue on Computer-Generated Music, Computer, 24(7), 22-28, 1991. 
10. Ebcioglu K.: An Efficient Logic Programming Language and Its Application to
   Music, in Lassez J.L.(ed.), Proceedings of the Fourth International Conference
   on Logic Programming - Volume 1, MIT Press, Cambridge, MA, pp.513-532, 1987. 
11. Freisleben B.: The Neural Composer: A Network for Musical Applications, in
   Aleksander I. and Taylor J.(eds.), Artificial Neural Networks, 2,
   North-Holland, Amsterdam, pp.1663-1666, 1992. 
12. Hardt D.: Computational Accounts of Music Understanding, Dept. of Computer and
   Information Science, University of Pennsylvania, Philadelphia, PA,
   MS-CIS-91-66, LINC LAB 207, 1991. 
13. Hipfinger G., Linster C.: Preprocessing of Musical Information and Examples of
   Applications for Neural Networks, in Trappl R.(ed.), Cybernetics and Systems
   '90, World Scientific Publishing, Singapore, pp.985-991, 1990. 
14. Howell P., West R., Cross I.(eds.): Representing Musical Structure, Academic
   Press, London, 1991. 
15. Jakobsson M.: Machine-generated Music with Themes, in Aleksander I. and Taylor
   J.(eds.), Artificial Neural Networks, 2, North-Holland, Amsterdam,
   pp.1645-1646, 1992. 
16. Johnson M.L.: Toward an Expert System for Expressive Musical Performance, in
   Special Issue on Computer-Generated Music, Computer, 24(7), 30-34, 1991. 
17. Jones J.A., Miller B.O., Scarborough D.L.: Discovering Grouping Structure in
   Music, in The Twelfth Annual Conference of the Cognitive Science Society,
   Lawrence Erlbaum, Hillsdale, NJ, pp.923-930, 1990. 
18. Kohonen T.: A Self-Learning Musical Grammar, or 'Associative Memory of the
   Second Kind', in IEEE International Conference On Neural Networks, Washington
   D.C., IEEE, Volume I, pp.1-6, 1989. 
19. Lerdahl F., Jackendoff R.: A Generative Theory of Tonal Music, MIT Press,
   Cambridge, MA, 1983. 
20. Linster C.: "GET RHYTHM" - A Musical Application for Neural Networks, GMD,
   Sankt Augustin, Germany, Arbeitspapier Nr.365, 1989. 
21. Machover T.: Hyperinstruments: A Composer's Approach to the Evolution of
   Intelligent Musical Instruments, in Jacobson L.(ed.), Cyberarts: Exploring Art
   & Technology, Miller Freeman Inc., Gilroy, CA, USA, pp.67-76, 1993. 
22. Marsden A., Pople A.(eds.): Representing Musical Structure, Academic Press,
   London, 1992. 
23. Mathews M.V., Pierce J.R.(eds.): Current Directions in Computer Music Research
   + Sound Examples (Compact Disc), MIT Press, Cambridge, MA, 1989. 
24. Mazzola G.: Geometry and Logic of Musical Performance, University of Zuerich,
   1993. 
25. Mazzola G.: Geometry and Logic of Musical Performance II: RUBATO, University
   of Zuerich, 1994. 
26. McAdams S., Deliege I.(eds.): Music and the Cognitive Sciences, Harwood,
   London, 1989. 
27. Minsky M.: Music, Mind, and Meaning, AI-Laboratory, Massachusetts Institute of
   Technology, Cambridge, MA, 1981. 
28. Mont-Reynaud B.: Problem-solving Strategies in a Music Transcription System,
   in Proceedings of the 9th International Joint Conference on Artificial
   Intelligence (IJCAI-85), Los Angeles, California, 1985. 
29. Morita H., Hashimoto S., Ohteru S.: A Computer Music System that Follows a
   Human Conductor, in Special Issue on Computer-Generated Music, Computer,
   24(7), 44-53, 1991. 
30. Mozer M.C.: Connectionist Music Composition Based on Melodic, Stylistic, and
   Psychophysical Constraints, University of Colorado at Boulder, Department of
   Computer Science, CU-CS-495-90, 1990. 
31. Mozer M.C., Soukup T.: Connectionist Music Composition Based on Melodic and
   Stylistic Constraints, in Lippmann R.P., et al.(eds.), Advances in Neural
   Information Processing 3, Morgan Kaufmann, San Mateo, CA, pp.789-796, 1991. 
32. Palmer C.: Structural Representations of Music Performance, in Proceedings of
   the Eleventh Anual Conference of the Cognitive Science Society, Lawrence
   Erlbaum, Hillsdale, NJ, pp.349-356, 1989. 
33. Port R., Anderson S.: Recognition of Melody Fragments in Continuously
   Performed Music, in Proceedings of the Eleventh Anual Conference of the
   Cognitive Science Society, Lawrence Erlbaum, Hillsdale, NJ, pp.820-827, 1989. 
34. Scarborough D., Manolios P., Jones J.: MusicSoar: Soar as an Architecture for
   Music Cognition, in Proceedings of the Fourteenth Annual Conference of the
   Cognitive Science Society, Lawrence Erlbaum, Hillsdale, NJ, pp.1104-1109,
   1992. 
35. Smoliar S.W.(ed.): The Role of Music in Multimedia, in IEEE Multimedia, IEEE
   Computer Society, Los Alamitos, CA, 1(1), 9-11, 1994. 
36. Tanguiane A.S.: Artificial Perception and Music Recognition: A Heuristic
   Approach, in Neumann B.(ed.), Proceedings of the Tenth European Conference on
   Artificial Intelligence (ECAI92), Wiley, Chichester, UK, pp.169-173, 1992. 
37. Todd P.M., Loy G.D.: Music and Connectionism, , 1991. 
38. Ungvary T., Waters S., Rajka P.: NUNTIUS: A Computer System for the
   Interactive Composition and Analysis of Music and Dance, Leonardo, 25(1)59-68,
   1992. 
39. Widmer G.: A Knowledge-Based Approach to Machine Learning in a Subfield of
   Tonal Music, in Proceedings of the ISSEK Workshop, Department of Mathematics
   and Computer Science, University of Udine, TR UDMI/RT/05/88, 1988. 
40. Widmer G.: A Knowledge-Intensive Approach to Machine Learning in Tonal Music:
   Learning to Write Two-Voice Counterpoint, First International Workshop on
   Artificial Intelligence and Music, Bonn, FRG, 1988. 
41. Widmer G.: Learning a Complex Musical Task on the Basis of a Plausible Theory
   of Musical Perception, Proceedings of the ECAI Workshop on Artificial
   Intelligence and Music, ECAI-90, Stockholm, Sweden, 1990. 
42. Widmer G.: Report on the ECAI-90 Workshop on Artificial Intelligence and
   Music, Interface 19(4), 293-296, 1990. 
43. Widmer G.: The Usefulness of Qualitative Theories of Musical Perception, 
   Proceedings of the International Computer Music Conference (ICMC-90), Glasgow,
   Scotland, 1990. 
44. Widmer G.: Learning by Plausible Reasoning and its Application to a Complex
   Musical Problem, in Michalski R.S. and Tecuci G.(eds.), Proceedings of the
   First International Workshop on Multistrategy Learning (MSL-91), Harpers
   Ferry, W.VA., 1991. 
45. Widmer G.: Qualitative Modelling in Music, First European Workshop on
   Qualitative Reasoning about Physical Systems, Genova, Italy, 1991. 
46. Widmer G.(ed.): Artificial Intelligence Models in Music, Fourth European
   Summer School in Logic, Language and Information, University of Essex,
   Colchester, U.K., 1992. 
47. Widmer G.(ed.): Artificial Intelligence and Music, 10th European Conference on
   Artificial Intelligence, Vienna, Austria, 1992. 
48. Widmer G.: A Knowledge Intensive Approach to Machine Learning in Music, in
   M.Balaban, K.Ebcioglu, O.Laske (eds.): Understanding Music with AI:
   Perspectives on Music Cognition, AAAI Press, Menlo Park, CA, 1992. 
49. Widmer G.: A Knowledge Intensive Approach to Machine Learning in Music,
   Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 
   TR-92-20, 1992. 
50. Widmer G.: Abstract Qualitative Perception Modelling and Intelligent Musical
   Learning, Oesterreichisches Forschungsinstitut fuer Artificial Intelligence,
   Wien, TR-92-21, 1992. 
51. Widmer G.: Qualitative Perception Modelling and Intelligent Musical Learning, 
   Computer Music Journal, 16(2)51-68, 1992. 
52. Widmer G.: The Importance of Musicologically Meaningful Vocabularies for
   Learning, Proceedings of the International Computer Music Conference (ICMC-92),
   San Jose, CA, 1992. 
53. Widmer G.: Modelling the Rational Basis of Musical Expression,
   Oesterreichisches Forschungsinstitut fuer Artificial Intelligence, Wien, 
   TR-93-20, 1993. 
54. Widmer G.: Understanding and Learning Musical Expression, Proceedings of the
   International Computer Music Conference (ICMC-93), Tokyo, Japan, 1993. 
55. Widmer G.: Cognitive Musicology: Musikwissenschaft, Cognitive Science und
   Artificial Intelligence, OeGAI-Journal, 12(3-4), 15-17, 1994. 
56. Widmer G.: Modelling the Rational Basis of Musical Expression, Computer Music
   Journal, 18 (in press), 1994. 
57. Widmer G.: Studying Musical Expression with AI and Machine Learning: "Analysis
   by Resynthesis", Papers of the Symposium on Generative Grammars for Music
   Performance, Royal Institute of Technology (KTH), Stockholm, 1994. 
58. Widmer G.: The Possible Roles of "Unconscious Knowledge" and Abstraction in
   Learning: Two Case Studies in the Domain of Tonal Music, Starting Conference,
   Scientific Programme "Learning in Humans and Machines", European Science
   Foundation, Sitges/Barcelona, 1994. 
59. Widmer G.: The Synergy of Music Theory and AI: Learning Multi-Level Expressive
   Interpretation, Proceedings of the 12th National Conference on Artificial
   Intelligence (AAAI-94), Seattle, WA, 1994. 
60. Widmer G.: The Synergy of Music Theory and AI: Learning Multi-Level Expressive
   Interpretation, Oesterreichisches Forschungsinstitut fuer Artificial
   Intelligence, Wien, TR-94-6, 1994.