Artículo
Rodriguez Zivic, P.H.; Shifres, F.; Cecchi, G.A. "Perceptual basis of evolving Western musical styles" (2013) Proceedings of the National Academy of Sciences of the United States of America. 110(24):10034-10038
Estamos trabajando para incorporar este artículo al repositorio
Consulte la política de Acceso Abierto del editor
Abstract:
The brain processes temporal statistics to predict future events and to categorize perceptual objects. These statistics, called expectancies, are found in music perception, and they span a variety of different features and time scales. Specifically, there is evidence that music perception involves strong expectancies regarding the distribution of a melodic interval, namely, the distance between two consecutive notes within the context of another. The recent availability of a large Western music dataset, consisting of the historical record condensed as melodic interval counts, has opened new possibilities for data-driven analysis of musical perception. In this context, we present an analytical approach that, based on cognitive theories of music expectation and machine learning techniques, recovers a set of factors that accurately identifies historical trends and stylistic transitions between the Baroque, Classical, Romantic, and Post-Romantic periods. We also offer a plausible musicological and cognitive interpretation of these factors, allowing us to propose them as data-driven principles of melodic expectation.
Registro:
| Documento: | Artículo |
| Título: | Perceptual basis of evolving Western musical styles |
| Autor: | Rodriguez Zivic, P.H.; Shifres, F.; Cecchi, G.A. |
| Filiación: | Computer Science Department, University of Buenos Aires, 1428 Buenos Aires, Argentina Laboratory for Music Experience Study, Faculty of Fine Arts, National University of La Plata, 1900 La Plata, Argentina Computational Biology Center, T. J. Watson IBM Research Center, Yorktown Heights, NY 10598, United States |
| Palabras clave: | Computational cognition; Culturomics; Pattern recognition; Psychology; article; cluster analysis; cognition; history; machine learning; music; music perception; perception; priority journal; probability; computational cognition; culturomics; pattern recognition; psychology; Acoustic Stimulation; Algorithms; Auditory Perception; Cognition; Computer Simulation; Humans; Models, Theoretical; Music; Pitch Perception |
| Año: | 2013 |
| Volumen: | 110 |
| Número: | 24 |
| Página de inicio: | 10034 |
| Página de fin: | 10038 |
| DOI: | http://dx.doi.org/10.1073/pnas.1222336110 |
| Título revista: | Proceedings of the National Academy of Sciences of the United States of America |
| Título revista abreviado: | Proc. Natl. Acad. Sci. U. S. A. |
| ISSN: | 00278424 |
| CODEN: | PNASA |
| Registro: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_00278424_v110_n24_p10034_RodriguezZivic |
Referencias:
- Sadie, S., Tyrrell, J., Levy, M., (2001) The New Grove Dictionary of Music and Musicians, 1084. , Macmillan, New York
- Serr, J., Corral, Bogu, M., Haro, M., Arcos, J.L., Measuring the evolution of contemporary western popular music (2012) Sci Rep, 2
- Narmour, E., (1990) The Analysis and Cognition of Basic Melodic Structures: The Implication-realization Model, , Univ. of Chicago Press, Chicago
- Carlsen, J., Some factors which influence melodic expectancy (1981) Psychomusicology: Music, Mind and Brain, 1 (1), pp. 12-29
- Schellenberg, G.E., Expectancy in melody: Tests of the implication-realization model (1996) Cognition, 58 (1), pp. 75-125
- Cuddy, L., Lunney, C., Expectancies generated by melodic intervals: Perceptual judgments of melodic continuity (1995) Perception & Psychophysics, 57 (4), pp. 451-462
- Viro, V., Peachnote: Music score search and analysis platform (2011) Proceedings of the International Society for Music Information Retrieval Conference (Miami), pp. 359-362
- Mitchell, T., (1997) Machine Learning, , McGraw-Hill, New York
- Wertheimer, M., (1938) Laws of Organization in Perceptual Forms, , Harcourt, Brace & Jovanovitch, London
- Lee, D.D., Seung, H.S., Learning the parts of objects by non-negative matrix factorization (1999) Nature, 401 (6755), pp. 788-791
- Ding, C., He, X., Simon, H.D., On the equivalence of nonnegative matrix factorization and spectral clustering (2005) Proc SIAM Data Mining Conf, 4
- Li, T., Ding, C., The relationships among various nonnegative matrix factorization methods for clustering (2006) Sixth International Conference on IEEE, , ICDM, 2006
- Choi, S., Algorithms for orthogonal nonnegative matrix factorization (2008) Neural Networks, 2008 (IEEE World Congress on Computational Intelligence), pp. 1828-1832
- Bukofzer, M.F., (1947) Music in the Baroque Era: From Monteverdi to Bach, 1084. , WW Norton, New York
- Simonton, D.K., Melodic structure and note transition probabilities: A content analysis of 15,618 classical themes (1984) Psychol Music, 12 (1), pp. 3-16
- Schoenberg, A., (1975) Style and Idea: Selected Writings of Arnold Schoenberg, pp. 216-217. , Univ of California Press, Berkley
- Huron, D.B., (2006) Sweet Anticipation: Music and the Psychology of Expectation, , MIT Press, Cambridge, MA
- Meyer, L.B., (1956) Emotion and Meaning in Music, , Univ of Chicago Press, Chicago
- Snyder, B., (2000) Music and Memory: An Introduction, , MIT Press, Cambridge, MA
- Thompson, W.F., Cuddy, L.L., Plaus, C., Expectancies generated by melodic intervals: Evaluation of principles of melodic implication in a melody-completion task (1997) Perception and Psychophysics, 59 (7), pp. 1069-1076
- Krumhansl, C.L., Music psychology and music theory: Problems and prospects (1995) Music Theory Spectrum, 17 (1), pp. 53-80
- Wertheimer, M., (1938) Laws of Organization in Perceptual Forms, , Harcourt, Brace & Jovanovitch, London
- Thompson, W.F., Stainton, M., Expectancy in Bohemian folk song melodies: Evaluation of implicative principles for implicative and closural intervals (1998) Music Perception, 15 (3), pp. 231-252
- Li, Y., Ngom, A., (2011) The Non-negative Matrix Factorization MATLAB Toolbox for Biological Data Mining, , Technical Report 11-060 (School of Computer Science, University of Windsor, Windsor, Canada)
- Prez-Sancho, C., Rizo, D., Iesta, J.M., Genre classification using chords and stochastic language models (2009) Conn Sci, 21 (23), pp. 145-159
Citas:
---------- APA ----------
Rodriguez Zivic, P.H., Shifres, F. & Cecchi, G.A. (2013) . Perceptual basis of evolving Western musical styles. Proceedings of the National Academy of Sciences of the United States of America, 110(24), 10034-10038.http://dx.doi.org/10.1073/pnas.1222336110
---------- CHICAGO ----------
Rodriguez Zivic, P.H., Shifres, F., Cecchi, G.A. "Perceptual basis of evolving Western musical styles" . Proceedings of the National Academy of Sciences of the United States of America 110, no. 24 (2013) : 10034-10038.http://dx.doi.org/10.1073/pnas.1222336110
---------- MLA ----------
Rodriguez Zivic, P.H., Shifres, F., Cecchi, G.A. "Perceptual basis of evolving Western musical styles" . Proceedings of the National Academy of Sciences of the United States of America, vol. 110, no. 24, 2013, pp. 10034-10038.http://dx.doi.org/10.1073/pnas.1222336110
---------- VANCOUVER ----------
Rodriguez Zivic, P.H., Shifres, F., Cecchi, G.A. Perceptual basis of evolving Western musical styles. Proc. Natl. Acad. Sci. U. S. A. 2013;110(24):10034-10038.http://dx.doi.org/10.1073/pnas.1222336110
