Název: Melodic segmentation: structure, cognition, algorithms
Zdrojový dokument: Musicologica Brunensia. 2017, roč. 52, č. 1, s. 53-61
Rozsah
53-61
-
ISSN1212-0391 (print)2336-436X (online)
Trvalý odkaz (DOI): https://doi.org/10.5817/MB2017-1-5
Trvalý odkaz (handle): https://hdl.handle.net/11222.digilib/136927
Type: Článek
Jazyk
Licence: Neurčená licence
Upozornění: Tyto citace jsou generovány automaticky. Nemusí být zcela správně podle citačních pravidel.
Abstrakt(y)
Segmentation of melodies into smaller units (phrases, themes, motifs, etc.) is an important process in both music analysis and music cognition. Also, segmentation is a necessary preprocessing step for various tasks in music information retrieval. Several algorithms for automatic segmentation have been proposed, based on different music-theoretical backgrounds and computing approaches. Rule-based models operate on a given set of logical conditions. Learning-based models, originating in linguistics, compute segmentation criteria on the basis of statistical parameters of a training corpus and/or of the given composition. The aim of this preliminary study is to propose and describe a new segmentation algorithm that is rule-based, parsimonious, and unambiguous.
Reference
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[2] BELLINGHAM, Jane. Phrase. The Oxford Companion to Music. Ed. Alison Latham. Oxford Music Online. Oxford University Press. Web. 20. 12. 2016. http://www.oxfordmusiconline.com/subscriber/article/opr/t114/e5148.
[3] BOD, Rens. Memory-based models of melodic analysis: Challenging the Gestalt principles. Journal of New Music Research, 2001, 30(3), pp. 27–36.
[4] BORODA, Moisej. Zur Bestimmung einer phrasenänlichen melodischen Informationseinheit in der Musik. In: Orlov, J.K., Boroda, M.G., Nadarejšvili, I.Š. (Eds.): Sprache, Text, Kunst. Quantitative Analysen (pp. 222–230). Bochum: Brockmeyer, 1982.
[5] BRENT, Michael. An efficient, probabilistically sound algorithm for segmentation and word discovery. Machine Learning, 1999a, 34(1–3), pp. 71–105. | DOI 10.1023/A:1007541817488
[6] BRENT, Michael. Speech segmentation and word discovery: A computational perspective. Trends in Cognitive Sciences, 1999b, 3(8), pp. 294–301. | DOI 10.1016/S1364-6613(99)01350-9
[7] CAMBOUROPOULOS, Emilios. Musical parallelism and melodic segmentation: A computational approach. Music Perception: An Interdisciplinary Journal, 2006, 23(3), pp. 249–268. | DOI 10.1525/mp.2006.23.3.249
[8] CAMBOUROPOULOS, Emilios. The local boundary detection model (LBDM) and its application in the study of expressive timing. In: Proceedings of the International Computer Music Conference (pp. 17–22). San Francisco: ICMA, 2001.
[9] DELIÈGE, Irène. Grouping conditions in listening to music: An approach to Lerdahl & Jackendoff's grouping preference rules. Music Perception: An Interdisciplinary Journal, 1987, 4(4), pp. 325–359. | DOI 10.2307/40285378
[10] DOWLING, William. Rhythmic groups and subjective chunks in memory for melodies. Perception and Psychophysics, 1973, 14(1), pp. 37–40. | DOI 10.3758/BF03198614
[11] DRABKIN, William. Motif. Grove Music Online. Oxford Music Online. Oxford University Press. Web. 20. 12. 2016. http://www.oxfordmusiconline.com/subscriber/article/grove/music/19221.
[12] FRANKLAND, Bradley – COHEN, Annabel. Parsing of melody: Quantification and testing of the local grouping rules of Lerdahl and Jackendoff's A Generative Theory of Tonal Music. Music Perception: An Interdisciplinary Journal, 2004, 21(4), pp. 499–543. | DOI 10.1525/mp.2004.21.4.499
[13] LERDAHL, Fred – JACKENDOFF, Ray. A Generative Grammar of Tonal Music. Cambridge, Mass.: The MIT Press, 1983.
[14] NARMOUR, Eugene. The Analysis and Cognition of Basic Melodic Structures: The Implication-Realization model. Chicago: University of Chicago Press, 1990.
[15] NARMOUR, Eugene. The Analysis and Cognition of Melodic Complexity: The Implication-Realization Model. Chicago: University of Chicago Press, 1992.
[16] PALMER, Caroline. Music performance. Annual Review of Psychology, 1997, 48(1), pp. 115–138. | DOI 10.1146/annurev.psych.48.1.115
[17] PATEL, Aniruddh – DANIELE, Joseph. An empirical comparison of rhythm in language and music. Cognition, 2003, 87(1), pp. B35-B45. | DOI 10.1016/S0010-0277(02)00187-7
[18] PEARCE, Marcus – MÜLLENSIEFEN, Daniel – WIGGINS, Geraint. Melodic grouping in music information retrieval: New methods and applications. In: Advances in music information retrieval. Springer, Berlin Heidelberg, 2010, pp. 364–388.
[19] REPP, Bruno H. Diversity and commonality in music performance: An analysis of timing micro-structure in Schumann's "Träumerei". The Journal of the Acoustical Society of America, 1992, 92(5), pp. 2546–2568. | DOI 10.1121/1.404425
[20] SCHENKER, Heinrich. Harmony. Chicago: University of Chicago Press, 1980.
[21] SLOBODA, John. The effect of item position on the likelihood of identification by inference in prose reading and music reading. Canadian Journal of Psychology, 1976, 30(4), pp. 228–237. | DOI 10.1037/h0082064
[22] TEMPERLEY, David. The Cognition of Basic Musical Structures. Cambridge, Mass., The MIT Press, 2001.
[23] WERTHEIMER, Max: Untersuchungen zur Lehre von der Gestalt II. Psychologische Forschung, 1923, 4(1), pp. 301–350. | DOI 10.1007/BF00410640