729
Views
1
CrossRef citations to date
0
Altmetric
Original Articles

A review of manual and computational approaches for the study of world music corpora

, ORCID Icon &
Pages 176-189 | Received 09 Jun 2017, Accepted 13 Dec 2017, Published online: 08 Jan 2018

References

  • Aarden, B. , & Huron, D. (2001). Mapping European Folksong: Geographical localization of musical features. Computing in Musicology , 12 , 169–183.
  • Abdallah, S. , Benetos, E. , Gold, N. , Hargreaves, S. , Weyde, T. , & Wolff, D. (2017). The Digital Music Lab: A big data infrastructure for digital musicology. ACM Journal on Computing and Cultural Heritage , 10 (1). doi:10.1145/2983918
  • Adler, G. (1885). Umfang, Methode und Ziel der Musikwissenschaft. Vierteljahresschrift für Musikwissenschaft , 1 (1),5–20.
  • Aucouturier, J. J. , Pachet, F. , & Sandler, M. (2005). “The way it sounds": Timbre models for analysis and retrieval of music signals. IEEE Transactions on Multimedia , 7 (6), 1028–1035. doi:10.1109/TMM.2005.858380
  • Baily, J. , & Collyer, D. M. (2006). Introduction: Music and migration. Journal of Ethnic and Migration Studies , 32 (2), 167–182.
  • Barrett, J. (1996). World music, nation and postcolonialism. Cultural Studies , 10 (2), 237–247.
  • Barz, G. F. , & Cooley, T. J. (Eds.). (2008). Shadows in the field: New perspectives for fieldwork in ethnomusicology . New York, NY: Oxford University Press.
  • Bel, B. , & Vecchione, B. (1993). Computational musicology. Computers and the Humanities , 27 (1), 1–5.
  • Bertin-Mahieux, T. , Ellis, D. P. W. , Whitman, B. , & Lamere, P. (2011). The million song dataset. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 591–596). Miami.
  • Blei, D. M. , & Lafferty, J. D. (2006). Dynamic topic models. In Proceedings of the 23rd International Conference on Machine Learning (ICML 2006) (pp. 113–120). Pittsburgh: ACM Press.
  • Bohlman, P. V. (2002). World music: A very short introduction . New York, NY: Oxford University Press.
  • Bozkurt, B. (2008). An automatic pitch analysis method for Turkish maqam music. Journal of New Music Research , 37 (1), 1–13.
  • Bronson, B. H. (1949). Mechanical help in the study of Folk song. Journal of American Folklore , 62 (244), 81–86.
  • Bronson, B. H. (1950). Some observations about melodic variation in British-American Folk tunes. Journal of the American Musicological Society , 3 , 120–134.
  • Bronson, B. H. (1972). The traditional tunes of the child ballads: With their texts, according to the extant records of Great Britain and America [4 Volumes] . Princeton, NJ: Princeton University Press.
  • Brown, S. , & Jordania, J. (2011). Universals in the world’s musics. Psychology of Music , 41 (2), 229–248.
  • Brown, S. , Savage, P. , Ko, M.-S. , Stoneking, M. , Ko, Y.-C. , Loo, J.-H. , & Trejaut, J. (2014). Correlations in the population structure of music, genes and language. Proceedings of the Royal Society B-Biological Sciences , 281 (1774). doi:20132072
  • Burgoyne, J. A. , Wild, J. , & Fujinaga, I. (2011). An expert ground-truth set for audio chord recognition and music analysis. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 633–638). Miami.
  • Burgoyne, J. A. , Wild, J. , & Fujinaga, I. (2013). Compositional data analysis of harmonic structures in popular music. In J. Yust , J. Wild , & J. A. Burgoyne (Eds.), Mathematics and computation in music. MCM 2013 . Lecture Notes in Computer Science (Vol. 7937, pp. 52–63). Berlin, Heidelberg: Springer.
  • Cabrera, J. J. , Díaz-báñez, J. M. , Escobar-Borrego, F. J. , Gómez, E. , Gómez, F. , & Mora, J. (2008). Comparative melodic analysis of a cappella flamenco cantes. In Fourth Conference on Interdisciplinary Musicology (CIM08) (pp. 1–8). Thessaloniki.
  • Clarke, D. (2014). On not losing heart: A response to savage and Brown’s “Toward a New Comparative Musicology". Analytical Approaches to World Music , 3 (2), 1–14.
  • Clayton, M. , Herbert, T. , & Middleton, R. (Eds.). (2003). The cultural study of music: A critical introduction . New York, NY: Routledge.
  • Conklin, D. , & Anagnostopoulou, C. (2011). Comparative pattern analysis of cretan folk songs. Journal of New Music Research , 40 (2), 119–125.
  • Cuthbert, M. S. , & Ariza, C. (2010). Music21: A toolkit for computer-aided musicology and symbolic music data. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 637–642). Utrecht.
  • Dahlig-Turek, E. , Klotz, S. , Parncutt, R. , & Wiering, F. (2012). Musicology (Re-) Mapped: Discussion Paper . European Science Foundation. Retrieved from http://www.esf.org/fileadmin/Public\_documents/Publications/musicology.pdf
  • Dong, G. , & Li, J. (1999). Efficient mining of emerging patterns: Discovering trends and differences. In Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 43–52). San Diego.
  • Downie, J. S. (2003). Music information retrieval. Annual Review of Information Science and Technology , 37 (1), 295–340.
  • Dubinskas, F. A. (1983). A musical Joseph’s coat: Patchwork patterns and social significance in world musics. Reviews in Anthropology , 10 (3), 27–42.
  • Feld, S. (1984). Sound structure as social structure. Ethnomusicology , 28 (3), 383–409.
  • Fillon, T. , Simonnot, J. , Mifune, M.-F. , Khoury, S. , Pellerin, G. , Le Coz, M. , ... Fourer, D. (2014). Telemeta: An open-source web framework for ethnomusicological audio archives management and automatic analysis. In 1st International Digital Libraries for Musicology Workshop (DLfM 2014) . London. doi:10.1145/2660168.2660169
  • Fink, R. (2013). Big (Bad) data . Retrieved from http://musicologynow.ams-net.org/2013/08/big-bad-data.html
  • Fourer, D. , Rouas, J.-L. , Hanna, P. , & Robine, M. (2014). Automatic timbre classification of ethnomusicological audio recordings. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 295–300).
  • Franzen, R. (2016). Europeana sounds: An interface into European sound archives. Sound Studies , 2 (1), 103–106. doi:10.1080/20551940.2016.1154303
  • Freeman, L. C. , & Merriam, A. P. (1956). Statistical classification in anthropology: An application to ethnomusicology. American Anthropologist , 58 , 464–472.
  • Futrelle, J. , & Downie, J. S. (2002). Interdisciplinary communities and research issues in Music Information Retrieval. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 215–221). Paris.
  • Ganguli, K. K. , Gulati, S. , Serra, X. , & Rao, P. (2016). Data-Driven Exploration of Melodic Structures in Hindustani Music. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 605–611). New York, NY.
  • Gómez, E. , Haro, M. , & Herrera, P. (2009). Music and geography: Content description of musical audio from different parts of the world. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 753–758). Kobe.
  • Gustar, A. J. (2014). Statistics in historical musicology (PhD thesis). Open University, Milton Keynes.
  • Hammersley, M. (2006). Ethnography: Problems and prospects. Ethnography and Education , 1 (1), 3–14. doi:10.1080/17457820500512697
  • Holzapfel, A. (2010). Similarity methods for computational ethnomusicology (Unpublished doctoral dissertation). University of Crete, Crete.
  • Holzapfel, A. , & Stylianou, Y. (2009). Rhythmic similarity in traditional Turkish music. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 99–104), Kobe.
  • Huron, D. (1996). The melodic arch in Western folksongs. Computing in Musicology , 10 , 3–23.
  • Inskip, C. , & Wiering, F. (2015). In their own words: Using text analysis to identify musicologists’ attitudes towards technology. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 455–461). Malaga.
  • International Folk Music Council . (1955). Resolutions: Definition of folk music (Vol. 7). Sao Paulo: International Council for Traditional Music.
  • Juhász, Z. (2006). A systematic comparison of different European folk music traditions using self-organizing maps. Journal of New Music Research , 35 (2), 95–112.
  • Juhász, Z. (2009). Automatic segmentation and comparative study of motives in eleven folk song collections using self-organizing maps and multidimensional mapping. Journal of New Music Research , 38 (1), 71–85.
  • Kroher, N. , Díaz-Báñez, J.-M. , Mora, J. , & Gómez, E. (2016, May). Corpus COFLA: A research corpus for the computational study of flamenco music. Journal on Computing and Cultural Heritage , 9 (2), 10:1–10:21. Retrieved from http://doi.acm.org/10.1145/2875428
  • Kroher, N. , Gómez, E. , Guastavino, C. , Gómez, F. , & Bonada, J. (2014). Computational models for perceived melodic similarity in a capella flamenco singing. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 65–70). Taipei.
  • Kruspe, A. , Lukashevich, H. , Abeßer, J. , Großmann, H. , & Dittmar, C. (2011). Automatic classification of musical pieces into global cultural areas. In AES 42nd International Conference (pp. 1–10). Ilmenau. doi:10.13140/2.1.4970.5606
  • Lartillot, O. , & Toiviainen, P. (2007). A Matlab toolbox for musical feature extraction from audio. In International Conference on Digital Audio Effects (pp. 237–244). Bordeaux.
  • Le Bomin, S. , Lecointre, G. , & Heyer, E. (2016). The evolution of musical diversity: The key role of vertical transmission. PLoS ONE , 11 (3), e0151570. doi:10.1371/journal.pone.0151570
  • Leman, M. (2008). Systematic musicology at the crossroads of modern music research. In Systematic and comparative musicology: Concepts, methods, findings (pp. 89–115). Frankfurt am Main: Peter Lang.
  • Lomax, A. (1968). Folk song style and culture . New Brunswick: American Association for the Advancement of Science.
  • Lomax, A. (1976). Cantometrics: An approach to the anthropology of music . Berkeley: University of California Extension Media Center.
  • Lomax, A. (1980). Factors of musical style. In S. Diamond (Ed.), Theory & practice: Essays presented to gene weltfish (pp. 29–58). The Hague: Mouton.
  • Lomax, A. , & Berkowitz, N. (1972). The evolutionary taxonomy of culture. Science , 177 (4045), 228–239. doi:10.1126/science.177.4045.228
  • Marolt, M. , Vratanar, J. F. , & Strle, G. (2009). Ethnomuse: Archiving folk music and dance culture. In IEEE EUROCON 2009 (pp. 322–326). St.-Petersburg.
  • Mauch, M. , MacCallum, R. M. , Levy, M. , & Leroi, A. M. (2015). The evolution of popular music: USA 1960–2010. Royal Society Open Science , 2 (5), 150081.
  • McFee, B. , McVicar, M. , Raffel, C. , Liang, D. , Nieto, O. , Battenberg, E. , .... Holovaty, A. (2015). Librosa: 0.4.1, Zenodo . doi:10.5281/zenodo.32193. Retrieved from http://github.com/bmcfee/librosa/tree/0.4.1
  • McKay, C. (2010). Automatic music classification with jMIR (PhD thesis). McGill University, Canada.
  • Moelants, D. , Cornelis, O. , & Leman, M. (2009). Exploring African tone scales. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 489–494). Kobe.
  • Mora, J. , Gómez, F. , Gómez, E. , Escobar-Borrego, F. , & Díaz-Báñez, J. M. (2010). Characterization and melodic similarity of a cappella flamenco cantes. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 351–356). Utrecht.
  • Nettheim, N. (1997). A bibliography of statistical applications in musicology. Musicology Australia , 20 (1), 94–106.
  • Nettl, B. (1970). Review of folk song style and culture by Alan Lomax source. American Anthropologist, New Series , 72 (2), 438–441.
  • Nettl, B. (2005). The harmless drudge: Defining ethnomusicology. In B. Nettl (Ed.), The study of ethnomusicology thirty-one issues and concepts (2nd ed., pp. 3–15). Urbana and Chicago: University of Illinois Press.
  • Nettl, B. (2015). The study of ethnomusicology: Thirty-three discussions (3rd ed.). Champaign: University of Illinois Press.
  • Nettl, B. , & Bohlman, P. V. (Eds.). (1991). Comparative musicology and anthropology of music: Essays on the history of ethnomusicology . Chicago: University of Chicago Press.
  • Nettl, B. , Stone, R. M. , Porter, J. , & Rice, T. (Eds.). (1998). The Garland encyclopedia of world music. (1998--2002 ed.). New York, NY: Garland Pub.
  • Neubarth, K. , Bergeron, M. , & Conklin, D. (2011). Associations between musicology and music information retrieval. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 429–434). Miami.
  • Pamjav, H. , Juhász, Z. , Zalán, A. , Németh, E. , & Damdin, B. (2012). A comparative phylogenetic study of genetics and folk music. Molecular Genetics and Genomics , 287 (4), 337–349.
  • Panteli, M. , Benetos, E. , & Dixon, S. (2016). Learning a feature space for similarity in world music. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 538–544). New York, NY.
  • Panteli, M. , & Dixon, S. (2016). On the evaluation of rhythmic and melodic descriptors for music similarity. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 468–474). New York, NY.
  • Panteli, M. , & Purwins, H. (2013). A quantitative comparison of chrysanthine theory and performance practice of scale tuning, steps, and prominence of the octoechos in byzantine chant. Journal of New Music Research , 42 (3), 205–221. doi:10.1080/09298215.2013.827215
  • Pegg, C. , Myers, H. , Bohlman, P. V. , & Stokes, M. (2001). Ethnomusicology. In Grove Music Online. Oxford Music Online. Retrieved from: http://oxfordindex.oup.com/view/10.1093/gmo/9781561592630.article.52178.
  • Porter, A. , Sordo, M. , & Serra, X. (2013). Dunya: A system for browsing audio music collections exploiting cultural context. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 101–106). Curitiba.
  • Prockup, M. , Ehmann, A. F. , Gouyon, F. , Schmidt, E. M. , Celma, O. , & Kim, Y. E. (2015). Modeling genre with the Music Genome project: Comparing human-labeled attributes and audio features. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 31–37). Malaga.
  • Raimond, Y. , Abdallah, S. , Sandler, M. , & Giasson, F. (2007). The music ontology. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 417–422). Vienna.
  • Rhodes, W. (1965). The use of computer in the classification of folk tunes. Studia Musicologica , VII , 339–343.
  • Russell, J. A. (2003). Core affect and the psychological construction of emotion. Psychological Review , 110 (1), 145–172.
  • Rzeszutek, T. , Savage, P. E. , & Brown, S. (2012). The structure of cross-cultural musical diversity. Proceedings of the Royal Society B-Biological Sciences , 279 (1733), 1606–1612.
  • Savage, P. E. (2017). Measuring the cultural evolution of music: With case studies of British-American and Japanese folk, art, and popular music (PhD thesis). Tokyo University of the Arts, Tokyo.
  • Savage, P. E. , & Brown, S. (2013). Toward a new comparative musicology. Analytical Approaches to World Music , 2 (2), 148–197.
  • Savage, P. E. , & Brown, S. (2014). Mapping music: Cluster analysis of song-type frequencies within and between cultures. Ethnomusicology , 58 (1), 133–155.
  • Savage, P. E. , Brown, S. , Sakai, E. , & Currie, T. E. (2015). Statistical universals reveal the structures and functions of human music. Proceedings of the National Academy of Sciences of the United States of America , 112 (29), 8987–8992.
  • Savage, P. E. , Matsumae, H. , Oota, H. , Stoneking, M. , Currie, T. E. , Tajima, A. , ... Brown, S. (2015). How circumpolar is Ainu music? Musical and genetic perspectives on the history of the Japanese archipelago. Ethnomusicology Forum , 24 (3), 443–467.
  • Savage, P. E. , Merritt, E. , Rzeszutek, T. , & Brown, S. (2012). CantoCore: A new cross-cultural song classification scheme. Analytical Approaches to World Music , 2 (1), 87–137.
  • Schaffrath, H. (1995). The Essen folksong collection in the Humdrum Kern Format (D. Huron, Ed.). Menlo Park, CA: Center for Computer Assisted Research in the Humanities.
  • Schedl, M. , Gomez, E. , & Urbano, J. (2014). Music information retrieval: Recent developments and applications. Foundations and Trends{\textregistered} in Information Retrieval , 8 (2–3), 127–261.
  • Schellenberg, E. G. , & von Scheve, C. (2012). Emotional cues in American popular music: Five decades of the Top 40. Psychology of Aesthetics, Creativity, and the Arts , 6 (3), 196–203.
  • Scherrer, D. K. , & Scherrer, P. H. (1971). An experiment in the computer measurement of melodic variation in folksong. The Journal of American Folklore , 84 (332), 230–241.
  • Serrà, J. , Corral, Á. , Boguñá, M. , Haro, M. , & Arcos, J. L. (2012). Measuring the evolution of contemporary western popular music. Scientific Reports , 2 (521). doi:10.1038/srep00521
  • Serra, X. (2011). A multicultural approach in music information research. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 151–156). Miami.
  • Serra, X. (2014). Creating research corpora for the computational study of music: The Case of the CompMusic Project. In AES 53rd International Conference: Semantic Audio . London.
  • Shalit, U. , Weinshall, D. , & Chechik, G. (2013). Modeling musical influence with topic models. In Proceedings of the International Conference on Machine Learning (pp. 244–252). Atlanta.
  • Shanahan, D. , Neubarth, K. , & Conklin, D. (2016). Mining musical traits of social functions in native American music. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 681–687). New York, NY.
  • Srinivasamurthy, A. , Holzapfel, A. , & Serra, X. (2014). In search of automatic rhythm analysis methods for Turkish and Indian art music. Journal of New Music Research , 43 (1), 94–114.
  • Stevens, C. J. (2012). Music perception and cognition: A review of recent cross-cultural research. Topics in Cognitive Science , 4 , 653–667. doi:10.1111/j.1756-8765.2012.01215.x
  • Temperley, D. , & Van Handel, L. (2013). Introduction to the special issues on corpus methods. Music Perception , 31 (1), 1–3.
  • Tenzer, M. (Ed.). (2006). Analytical studies in world music . New York, NY: Oxford University Press.
  • Thompson, D. (2015). 1991: The most important year in pop-music history. The Atlantic . Retrieved from http://www.theatlantic.com/entertainment/archive/2015/05/1991-the-most-important-year-in-music/392642/
  • Tzanetakis, G. , & Cook, P. (2000). MARSYAS: A framework for audio analysis. Organised Sound , 4 (3), 169–175.
  • Tzanetakis, G. , Kapur, A. , Schloss, A. W. , & Wright, M. (2007). Computational ethnomusicology. Journal of Interdisciplinary Music Studies , 1 (2), 1–24.
  • Underwood, T. (2015). Can we date revolutions inthe history of literature and music? . Retrieved from https://tedunderwood.com/2015/10/03/can-we-date-revolutions-in-the-history-of-literature-and-music/
  • Underwood, T. , Long, H. , So, R. J. , & Zhu, Y. (2016). You say you found a revolution . Retrieved from https://tedunderwood.com/2016/02/07/you-say-you-found-a-revolution/
  • Urbano, J. , Bogdanov, D. , Herrera, P. , Gómez, E. , & Serra, X. (2014). What is the effect of audio quality on the robustness of MFCCs and chroma features. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 573–578). Taipei.
  • van Kranenburg, P. , Volk, A. , & Wiering, F. (2013). A comparison between global and local features for computational classification of folk song melodies. Journal of New Music Research , 42 (1), 1–18.
  • van Kranenburg, P. , Garbers, J. , Volk, A. , Wiering, F. , Grijp, L. , & Veltkamp, R. C. (2010). Collaborative perspectives for folk song research and music information retrieval: The indispensable role of computational musicology. Journal of Interdisciplinary Music Studies , 4 (1), 17–43.
  • Viro, V. (2011). Peachnote: Music score search and analysis platform. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 359–362). Miami.
  • Volk, A. , & de Haas, W. B. (2013). A corpus-based study on ragtime syncopation. In Proceedings of the International Society for Music Information Retrieval Conference (pp. 163–168). Curitiba.
  • Volk, A. , & van Kranenburg, P. (2012). Melodic similarity among folk songs: An annotation study on similarity-based categorization in music. Musicae Scientiae , 16 (3), 317–339.
  • Volk, A. , Wiering, F. , & van Kranenburg, P. (2011). Unfolding the potential of computational musicology. In Proceedings of the 13th International Conference on Informatics and Semiotics in Organisations (ICISO) (pp. 137–144). Leeuwarden.
  • von Hornbostel, E. M. , & Sachs, C. (1961). Classification of musical instruments. Galpin Society Journal , 14 , 3–29.
  • Wallmark, Z. (2013). Big data and musicology: New methods, new questions . Retrieved from http://www.academia.edu/6442281/Big\_Data\_and\_Musicology\_New\_Methods\_New\_Questions
  • Walshaw, C. (2014). A statistical analysis of the ABC music notation corpus : Exploring duplication. In Proceedings of the Fourth International Workshop on Folk Music Analysis (pp. 1–8). Istanbul. doi:10.13140/2.1.4340.0961
  • Wiering, F. , & Benetos, E. (2013). Digital musicology and MIR : Papers, projects and challenges. In ISMIR 2013 late-breaking session . Retrieved from http://ismir2013.ismir.net/wp-content/uploads/2014/02/lbd4.pdf
  • Zhou, F. , Claire, Q. , & King, R. D. (2014). Predicting the geographical origin of music. In IEEE International Conference on Data Mining (pp. 1115–1120). Shenzhen.
  • Zivic, P. H. R. , 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), 10034-8.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.