Improvisation Planning and Jam Session Design using concepts of Sequence Variation and Flow Experience

Conference object English OPEN
Dubnov, Shlomo; Assayag, Gérard;
  • Publisher: HAL CCSD
  • Journal: issn: 2518-3672
  • Publisher copyright policies & self-archiving
  • Related identifiers: doi: 10.5281/zenodo.849279, doi: 10.5281/zenodo.849278
  • Subject: [INFO.INFO-PL]Computer Science [cs]/Programming Languages [cs.PL] | [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] | [ INFO.INFO-SD ] Computer Science [cs]/Sound [cs.SD] | [ STAT.ML ] Statistics [stat]/Machine Learning [stat.ML] | [SHS.MUSIQ]Humanities and Social Sciences/Musicology and performing arts | Informatique musicale | [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing | [ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing | NA | [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD] | [ INFO.INFO-FL ] Computer Science [cs]/Formal Languages and Automata Theory [cs.FL] | [ INFO.INFO-PL ] Computer Science [cs]/Programming Languages [cs.PL] | [STAT.ML]Statistics [stat]/Machine Learning [stat.ML] | [INFO.INFO-FL]Computer Science [cs]/Formal Languages and Automata Theory [cs.FL] | [ INFO.INFO-AI ] Computer Science [cs]/Artificial Intelligence [cs.AI] | [ SHS.MUSIQ ] Humanities and Social Sciences/Musicology and performing arts

cote interne IRCAM: Assayag05a; National audience; We describe a model for improvisation design based on Factor Oracle automation, which is extended to perform learning and analysis of incoming sequences in terms of sequence variation parameters, namely replication, rec... View more
  • References (10)

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    [2] Assayag, G., Dubnov, S., 2004, Using Factor Oracles for Machine Improvisation, Soft Computing 8, pp. 1432-7643, September.

    [3] Brand, M., and Hertzmann, A, 2000, Style Machines, In Proceedings of SIGGRAPH 2000, New Orleans, Louisiana, USA

    [4] Csikszentmihalyi, M., 1990, Flow: The psychology of Optimal Experience, Harper & Row Publishers, Inc

    [5] Dubnov, S., Assayag, G., Lartillot, O., Bejerano, G., 2003, Using Machine-Learning Methods for Musical Style Modeling, IEEE Computer, October 2003, Vol. 10, n° 38, p.73-80.

    [6] Dubnov S., S.McAdams and R. Reynolds, Structural and Affective Aspects of Music from Statistical Audio Signal Analysis, Journal of the American Society of Information Science and Technology, (in press), 2005.

    [7] Foote, J. and M. Cooper., Media Segementation using Self-Similarity Decomposition., Proc. SPIE, 5021:167--75, 2003

    [8] Freund Y., Ron, D., Learning to model sequences generated by switching distributions, Proceedings of the eighth annual conference on Computational learning theory, p.41-50, July 05-08, 1995, Santa Cruz, California, United States

    [9] Perlis D., and K. Purang and D. Purushothaman and C. Andersen and D. Traum. Modeling time and meta-reasoning in dialogue via active logic. 1999. Working notes of AAAI Fall Symposium on Psychological Models of Communication.

    [10] Rueda C., G. Alvarez, L.O.Quesada, G. Tamura, F. Valencia, J. F. Diaz, G. Assayag, Integrating Constraints and Concurrent Objects in Musical Applications: A Calculus and its Visual Language, Constraints 6, 21-51, 2001.

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