A Search for Structural Similarities of Oral Musical Traditions in Eurasia and America Using the Self Organizing Cloud Algorithm

References

• Aarden, B., & Huron, D. (2001). Mapping European folksong: Geographical localization of musical features. Computing in Musicology, 12, 169–183.
   Google Scholar
• Ali-MacLachlan, I., Köküer, M., Jancovic, P., Williams, I., & Athwal, C. (2013). Quantifying timbral variations in traditional Irish flute playing. In P. van Kranenburg, C. Anagnostopoulou, & A. Volk (Eds.), Proceedings of the Third International Workshop on Folk Music Analysis, June 6–7, 2013. Amsterdam, Netherlands (pp. 7–14). Amsterdam: Meertens Institute, Utrecht: Department of Information and Computing Sciences, Utrecht University.
   Google Scholar
• Aloupis, G., Fevens, T., Langerman, S., Matsui, T., Mesa, A., Nunez, Y., ... Toussaint, G. T. (2006). Algorithms for computing geometric measures of melodic similarity. Computer Music Journal, 30(3), 67–76.
   Web of Science ®Google Scholar
• Bartók, B. (1937). Folk song research and nationalism. Reprinted in Benjamin Suchoff (Ed.) (1976), Béla Bartók Essays. London: Faber and Faber.
   Google Scholar
• Bartók, B. (1949). On collecting folk songs in Turkey. Tempo, New Ser., No. 13, Bartók Number, (Autumn), 15–19+38
   Google Scholar
• Bartók, B. (1976). Turkish Folk Music from Asia Minor, ed. Benjamin Suchoff, with an afterword by Kurt Reinhard, Princeton: Princeton University Press.
   Google Scholar
• Bayard, S.P. (1950). Prolegomena to a study of the principal melodic families of British-American folk song. Journal of American Folklore, 63 (247), 1–44.
   Google Scholar
• Bello, J. P. (2011). Measuring structural similarity in music. IEEE Transactions on Audio, Speech, and Language, 19(7), 2013–2025.
   Web of Science ®Google Scholar
• Bigand, E., Vieillard, S., Madurell, F., Marozeau, J., & Dacquet, A. (2005). Multidimensional scaling of emotional responses to music: The effect of musical expertise and of the duration of the excerpts. Cognition and Emotion, 19(8), 1113–1139.
   Web of Science ®Google Scholar
• Borg, I., & Groenen, P. (2005). Modern multidimensional scaling: Theory and applications (2nd ed.). New York: Springer-Verlag.
   Google Scholar
• Brown, S., Savage, P.E., Ko, A.M.-S., Stoneking, M., Ko, Y.-C., Loo, J.-H., & Trejaut, J.A. (2013). Correlations in the population structure of music, genes and language. Proceedings of the Royal Society B [online], 281(1774), 20132072. doi:10.1098/rspb.2013.2072
   PubMed Web of Science ®Google Scholar
• Cross, I. (2001). Music, cognition, culture, and evolution. Annals of the New York Academy of Sciences, 930, 28–42.
   PubMed Web of Science ®Google Scholar
• Cross, I. (2009). The evolutionary nature of musical meaning. Musicae Scientiae, 13(2 suppl), 179–200.
   Web of Science ®Google Scholar
• Csébfalvy, K., Havass, M., Járdányi, P., & Vargyas, L. (1965). Systematization of tunes by computers, VII 253–257.
   Google Scholar
• Conklin, D. (2013). Folk tune classification with multiple viewpoints. In P. van Kranenburg, C. Anagnostopoulou, & A. Volk (Eds.), Proceedings of the Third International Workshop on Folk Music Analysis, June 6–7, 2013 (pp. 99–100). Amsterdam: Utrecht University.
   Google Scholar
• Corpus Musicae Popularis Hungaricae l-XII. (1951–2012), Budapest: Balassi Institute.
   Google Scholar
• Cowdery, J.R. (1984). A fresh look at the concept of tune family. Ethnomusicology, 28(3), 495–504.
   Web of Science ®Google Scholar
• Dahlig-Turek, E. (2013). Analysis of ‘Polish rhythms’. In P. van Kranenburg, C. Anagnostopoulou, & A. Volk (Eds.), Proceedings of the Third International Workshop on Folk Music Analysis, June 6–7, 2013. Amsterdam, Netherlands (pp. 104–105). Amsterdam: Meertens Institute, Utrecht: Department of Information and Computing Sciences, Utrecht University.
   Google Scholar
• Demartines, P., & Herault, J. (1997). Curvilinear component analysis: A self-organizing neural network for nonlinear mapping of data sets. IEEE Transactions on Neural Networks, 8(1), 148–154.
   PubMed Web of Science ®Google Scholar
• De Poli, G., & Prandoni, P. (1997). Sonological models for timbre characterization. Journal of New Music Research, 26, 170–97.
   Google Scholar
• Dobszay, L., & Szendrei, J. (1988). A magyar népdaltípusok katalógusa [Catologue of Hungarian folksong types] (In Hungarian). Budapest: MTA Zenetudományi Intézet.
   Google Scholar
• Eastlund, J. O. (1992). A multidimensional scaling analysis of musical style. Journal of Research in Music Education, 40(3), 204–215.
   Web of Science ®Google Scholar
• Eerola, T., Järvinen, T., Louhivuori, J., & Toiviainen, P. (2001). Statistical features and perceived similarity of folk melodies. Music Perception, 18, 275–296.
   Web of Science ®Google Scholar
• Estévez, P. A., & Figuerora, C. J. (2006). Online data visualization using the neural gas network. Neural Networks, 19, 923–934.
   PubMed Web of Science ®Google Scholar
• Freeman, I., & Merriam, A. (1956). Statistical classification in anthropology: an application to ethnomusicology. American Anthropologist, 58, 464–472.
   Web of Science ®Google Scholar
• Forgy, E.W. (1965). Cluster analysis of multivariate data: Efficiciency versus interpretability of classifications. Biometrics, 21, 768–769.
   Web of Science ®Google Scholar
• Fouloulis, T., Pikrakis, A., & Cambouropoulos, E. (2013). Traditional asymmetric rhythms: a refined model of meter induction based on asymmetric meter templates. In P. van Kranenburg, C. Anagnostopoulou, & A. Volk (Eds.), Proceedings of the Third International Workshop on Folk Music Analysis, June 6–7, 2013. Amsterdam, Netherlands (pp. 28–33). Amsterdam: Meertens Institute, Utrecht: Department of Information and Computing Sciences, Utrecht University.
   Google Scholar
• Goebel, T. (2008). The Late Pleistocene dispersal of modern humans in the Americas. Science, 319(5869), 1497–1502.
   PubMed Web of Science ®Google Scholar
• Grey, J.M. (1977). Multidimensional perceptual scaling of musical timbres. Journal of the Acoustical Society of America, 61(5), 1270–1277.
   PubMed Web of Science ®Google Scholar
• Hourdin, C., Charbonneau, G., & Moussa, T. (1997a). A multidimensional scaling analysis of musical instruments’ time-varying spectra. Computer Music Journal, 21(2), 40–55.
   Web of Science ®Google Scholar
• Hourdin, C., Charbonneau, G., & Moussa, T. (1997b). A sound-synthesis technique based on multidimensional scaling of spectra. Computer Music Journal, 21(2), 56–68.
   Web of Science ®Google Scholar
• Huron, D. (1996). The melodic arch in Western folksongs. Computing in Musicology, 10, 3–23.
   Google Scholar
• Járdányi, P. (1974). Experiences and results in systematizing Hungarian folksongs (pp. 17–20). XXII: Studia Musicologica.
   Google Scholar
• Juhász, Z. (2000). A model of variation in the music of a Hungarian ethnic group. Journal of New Music Research, 29(2), 159–172.
   Web of Science ®Google Scholar
• Juhász, Z. (2006). A systematic comparison of different European folk music traditions using self-organising maps. Journal of New Music Research, 35(2), 95–112.
   Web of Science ®Google Scholar
• Juhász, Z. (2011). Low dimensional visualisation of folk music systems using the self organising cloud. Proceedings of 12th International Society for Music Information Retrieval (ISMIR 2011) (pp. 299–304). Canada: International Society for Music Information Retrieval.
   Google Scholar
• Juhász, Z. (2012). A mathematical study of note association paradigms in different folk music cultures. Journal of Mathematics and Music, 6(3), 169–185.
   Web of Science ®Google Scholar
• Juhász, Z., & Sipos, J. (2009). A comparative analysis of Eurasian folksong corpora, using self organising maps. Journal of Interdisciplinary Music Studies, Advance online publication doi:10.4407/jims.2009.11.005
   Google Scholar
• Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., & Wu, A.Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 881–892. doi:10.1109/TPAMI.2002.1017616
   Web of Science ®Google Scholar
• Kodály, Z. (1971). Folk music of Hungary. Budapest: Corvina.
   Google Scholar
• Kohonen, T. (1995). Self-organising maps. Berlin: Springer-Verlag.
   Google Scholar
• Krohn, I. (1899). Über die Art und Entstehung der geistlichen Volksmelodien in Finland. Helsingfors: Väitöskirja.
   Google Scholar
• Lartillot, O., Yazıcı, Y.F., & Mungan, E. (2013). A more informative segmentation model, empirically compared with state of the art on traditional Turkish music. In P. van Kranenburg, C. Anagnostopoulou, & A. Volk (Eds.), Proceedings of the Third International Workshop on Folk Music Analysis, June 6–7, 2013. Amsterdam, Netherlands (pp. 63–70). Amsterdam: Meertens Institute, Utrecht: Department of Information and Computing Sciences, Utrecht University.
   Google Scholar
• Lee, J. A., Lendasse, A., & Verleysen, M. (2004). Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis. Neurocomputing, 57, 49–76.
   Web of Science ®Google Scholar
• Leman, M. (2000). An auditory model of the role of short-term memory in probe-tone ratings. Music Perception, 17(4), 481–509.
   Web of Science ®Google Scholar
• Lomax, A. (1968). Folk song style and culture. With contributions by Conrad Arensberg, Edwin E. Erickson, Victor Grauer, Norman Berkowitz, Irmgard Bartenieff, Forrestine Paulay, Joan Halifax, Barbara Ayres, Norman N. Markel, Roswell Rudd, Monika Vizedom, Fred Peng, Roger Wescott, David Brown (American Association for the Advancement of Science, Publication no. 88). Washington, DC: Colonial Press.
   Google Scholar
• Martinetz, T., & Schulten, K. (1991). A ‘neural gas’ network learns topologies. In T. Kohonen, K. Mäkisara, O. Simula, & J. Kangas (Eds.), Artificial Neural Networks (pp. 397–402). Amsterdam: Elsevier.
   Google Scholar
• Müllensiefen, D., & Frieler, K. (2004a). Cognitive adequacy in the measurement of melodic similarity: Algorithmic vs. human judgments. Computing in Musicology, 13, 147–176.
   Google Scholar
• Müllensiefen, D., & Frieler, K. (2004b). Optimizing measures of melodic similarity for the exploration of a large folk song database. Proceedings of the ISMIR 2004 (8pp). Barcelona: Universitat Pompeu Fabra.
   Google Scholar
• Müller, M., Grosche, P., & Jiang, N. (2011). A segment-based fitness measure for capturing repetitive structures of music recordings. Proceedings of 12th ISMIR Conference (ISMIR 2011) (pp. 615–620). Canada: International Society for Music Information Retrieval.
   Google Scholar
• Nettl, B. (1965/1989). Folk and traditional music of the Western continents. Engelwood Cliffs, NJ: Prentice-Hall.
   Google Scholar
• Nettl, B. (2005). The study of ethnomusicology: Thirty-one issues and concepts. Champaign, IL: University of Illinois Press.
   Google Scholar
• Pamjav, H., Juhász, Z., Németh, E., & Zalán, A. (2012). A comparative phylogenetic study of genetics and folk music. Molecular Genetics and Genomics, 287(4), 337–349. doi:10.1007/s00438-012-0683-y
   PubMed Web of Science ®Google Scholar
• Samson, S., Zatorre, R. J., & Ramsay, J. (1997). Multidimensional scaling of synthetic musical timbre: Perception of spectral and temporal characteristics. Canadian Journal of Experimental Psychology, 51(4), 307–315.
   PubMed Web of Science ®Google Scholar
• Schmuckler, M.A. (1999). Testing models of melodic contour similarity. Music Perception, 16(3), 109–150.
   Web of Science ®Google Scholar
• Selfridge-Field, E. (2004). Towards a measure of cognitive distance in melodic similarity. Computing in Musicology, 13, 93–112.
   Google Scholar
• Six, J., & Cornelis, O. (2013). Computer-assisted transcription of ethnic music. In P. van Kranenburg, C. Anagnostopoulou, & A. Volk (Eds.), Proceedings of the Third International Workshop on Folk Music Analysis, June 6–7, 2013. Amsterdam, Netherlands (pp. 71–73). Amsterdam: Meertens Institute, Utrecht: Department of Information and Computing Sciences, Utrecht University.
   Google Scholar
• Szendrei, J., Dobszay, L., & Rajeczky, B. (1979). XVI-XVII. századi dallamaink a népi emlékezetben [Hungarian melodies transcribed in the 16-17th centuries living in the musical folklore]. Budapest: Akadémiai Kiadó.
   Google Scholar
• Taguchi, Y.H., & Oono, Y. (2005). Gene expression relational patterns of gene expression via non-metric multidimensional scaling analysis. Bioinformatics, 21(6), 730–740.
   PubMed Web of Science ®Google Scholar
• Toiviainen, P. (1996). Optimizing auditory images and distance metrics for self-organizing timbre maps. Journal of New Music Research, 25, 1–30.
   Web of Science ®Google Scholar
• Toiviainen, P. (2000). Symbolic AI versus connectionism in music research. In E. Mirinda (Ed.), Readings in music and artificial intelligence. Amsterdam: Harwood Academic Publishers.
   Google Scholar
• Toiviainen, P., & Eerola, T. (2002). A computational model of melodic similarity based on multiple representations and self-organizing maps. In C. Stevens, D. Burham, G. McPherson, E. Schubert, & J. Rewick (Eds.), Proceedings of the 7th International Conference on Music Perception and Cognition, Sidney, 2002 (pp. 236–239). Adelaide: Causal Productions.
   Google Scholar
• Typke, R., Wiering, F., & Veltkamp, R.C. (2007). Transportation distances and human perception of melodic similarity. Musicae Scientiae, Discussion Forum, 4A, 153–182.
   Google Scholar
• Van Kranenburg, P., Garbers, J., Volk, A., Wiering, F., Grijp, L.P., & Veltkamp, R.C. (2009a). Collaboration perspectives for folk song research and music information retrieval: The indispensable role of computational musicology. Journal of Interdisciplinary Music Studies, 4(1), 17–43.
   Google Scholar
• Van Kranenburg, P., Volk, A., Wiering, F., & Veltkamp, R.C. (2009b). Musical models for folk-song melody alignment. In K. Hirata, G. Tzanetakis, & K. Yoshii (Eds.), 10th International Society for Music Information Retrieval Conference (ISMIR 2009) (pp. 507–512). Canada: International Society for Music Information Retrieval.
   Google Scholar
• Vargyas, L. (1980). The prehistory of Hungarian folk music. (In Hungarian. A magyar zene őstörténete I-II.) Ethnographia 91 (1), 1–34; 91(2), 192--236.
   Google Scholar
• Velarde, G., Weyde, T., & Meredith, D. (2013). Wavelet-filtering of symbolic music representations for folk tune segmentation and classification. In P. van Kranenburg, C. Anagnostopoulou, & A. Volk (Eds.), Proceedings of the Third International Workshop on Folk Music Analysis, June 6–7, 2013. Amsterdam, Netherlands (pp. 56–62). Amsterdam: Meertens Institute, Utrecht: Department of Information and Computing Sciences, Utrecht University.
   Google Scholar
• Vikár, L. (1958). Chinese folksongs with answers at the interval of a fifth (pp. 430–432). VII: Acta Ethnographica.
   Google Scholar
• Vikár, L., & Bereczki, G. (1971). Cheremis folksongs. Budapest: Akadémiai Kiadó.
   Google Scholar
• Vikár, L., & Bereczki, G. (1979). Chuvashs folksongs. Budapest: Akadémiai Kiadó.
   Google Scholar
• Wiora, W. (1950). Europäischer Volksgesang. Gemeinsame Formen in charakteristischen Abwandlungen. Köln: Arno Volk Verlag.
   Google Scholar
• Zhang, J., Harbottle, G., Wang, Ch, & Kong, Zh. (1999). Oldest playable musical instruments found at Jiahu early Neolithic site in China. Nature, 401, 366–368.
   PubMed Web of Science ®Google Scholar