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Title
Abstract
Figures
Introduction
Musical Databases
Data Mining
Conclusion
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Art
Music

Visualization in comparative music research

Profile image of Tuomas EerolaTuomas Eerola
https://doi.org/10.1007/978-3-7908-1709-6_16
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Abstract
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This paper examines various aspects of comparative music research with a focus on visualization techniques. It discusses categories of music representation including notation-based, event-based, and signal representations, as well as the computational tools available for analyzing each type. The paper also highlights the significance of musical databases such as the RISM incipits database and the Digital Archive of Finnish Folk Tunes, providing examples of visualization methods applied to large musical collections.

Figures (6)
Fig. 1. The component planes of a SOM trained with pitch class distributions 19412 folk melodies. Pitch-class distributions can be used, for instance, to infer the key and mode of a piece of music. They also enable a more detailed analysis on importance of different tones in the musical material. Fig. 1 displays component planes of a SOM with 12 x 18 cells pitch-class dis and 8613 Finnish melodies. The musical feature SOM thus had ure correspond 19412 x s to one 2 components. Each of t pitch-class, from C to B, the value of the respective component in the cel red colour standing for a high value, and t For instance, t type vectors h he lower aving hig quently, melodies in w mapped to this region. eft region of the SOM c h values for the pitch c ha ne S on was trained with ributions of 2240 Chinese, 2323 Hungarian, 6236 German, matrix used to train 2 subplots of the fig- and the colour displays prototype vectors, ne ne ne ne ne ne he blue colour for a low value. ains cells with proto- asses G and A. Conse- hich these pitch-classes are frequently used are
Fig. 1. The component planes of a SOM trained with pitch class distributions 19412 folk melodies. Pitch-class distributions can be used, for instance, to infer the key and mode of a piece of music. They also enable a more detailed analysis on importance of different tones in the musical material. Fig. 1 displays component planes of a SOM with 12 x 18 cells pitch-class dis and 8613 Finnish melodies. The musical feature SOM thus had ure correspond 19412 x s to one 2 components. Each of t pitch-class, from C to B, the value of the respective component in the cel red colour standing for a high value, and t For instance, t type vectors h he lower aving hig quently, melodies in w mapped to this region. eft region of the SOM c h values for the pitch c ha ne S on was trained with ributions of 2240 Chinese, 2323 Hungarian, 6236 German, matrix used to train 2 subplots of the fig- and the colour displays prototype vectors, ne ne ne ne ne ne he blue colour for a low value. ains cells with proto- asses G and A. Conse- hich these pitch-classes are frequently used are
Fig. 2. Number of melodies mapped to each cell of the SOM of Fig. 2 for each of the four collections.
Fig. 2. Number of melodies mapped to each cell of the SOM of Fig. 2 for each of the four collections.
Fig. 3. Visualization of metrical structures in (a) the Digital Archive of Finnish Folk Tunes, and its (b) Folk songs and (c) Rune songs subcollections. Fig. 3a displays a visualization of metrical structures in a collection of Finnish folk melodies. To obtain the visualization, 8613 melodies from the Digital Archive of Finnish Folk Tunes (Eerola & Toiviainen 2004b) were subjected to autocorrelation analysis, using the method of Toiviainen & Eerola (2006). This resulted in 32-component autocorrelation vectors, rep- resenting the metrical structure of each melody in the collection. Subse- quently, PP and kernel density estimation were applied to the projection.
Fig. 3. Visualization of metrical structures in (a) the Digital Archive of Finnish Folk Tunes, and its (b) Folk songs and (c) Rune songs subcollections. Fig. 3a displays a visualization of metrical structures in a collection of Finnish folk melodies. To obtain the visualization, 8613 melodies from the Digital Archive of Finnish Folk Tunes (Eerola & Toiviainen 2004b) were subjected to autocorrelation analysis, using the method of Toiviainen & Eerola (2006). This resulted in 32-component autocorrelation vectors, rep- resenting the metrical structure of each melody in the collection. Subse- quently, PP and kernel density estimation were applied to the projection.
Fig. 4. The prototype vectors of a SOM trained with 23557 melodic contour vec- tors. Melodic contour, or the overall temporal development of the pitch height, is one of the most salient features of a melody (Dowling 1971). Some shapes of melodic contour shapes have been found to be more frequent han others. For instance, Huron (1996) investigated the melodies of the Essen collection and found that an arch-shaped (i.e. ascending pitch fol- owed by descending pitch) contour was the most frequent contour form in he collection. The SOM can be used to study and visualize typical contour shapes. Fig. 4 displays a SOM with 6 x 9 cells that was trained with 64- component melodic contour vectors. The material consisted of 9696 me- odic phrases from Hungarian folk melodies and 13861 melodic phrases from German folk melodies. The musical feature matrix thus had 23557 x 64 components. The prototype vectors of the obtained SOM are displayed in Fig. 4. As can be seen, the arch-shaped contour is prevalent on the right side of the map, but the left side of the map is partly occupied by descend- ing and ascending contours.
Fig. 4. The prototype vectors of a SOM trained with 23557 melodic contour vec- tors. Melodic contour, or the overall temporal development of the pitch height, is one of the most salient features of a melody (Dowling 1971). Some shapes of melodic contour shapes have been found to be more frequent han others. For instance, Huron (1996) investigated the melodies of the Essen collection and found that an arch-shaped (i.e. ascending pitch fol- owed by descending pitch) contour was the most frequent contour form in he collection. The SOM can be used to study and visualize typical contour shapes. Fig. 4 displays a SOM with 6 x 9 cells that was trained with 64- component melodic contour vectors. The material consisted of 9696 me- odic phrases from Hungarian folk melodies and 13861 melodic phrases from German folk melodies. The musical feature matrix thus had 23557 x 64 components. The prototype vectors of the obtained SOM are displayed in Fig. 4. As can be seen, the arch-shaped contour is prevalent on the right side of the map, but the left side of the map is partly occupied by descend- ing and ascending contours.
Fig. 5. Number of melodic phrases mapped to each cell of the SOM of Fig. 5 fo both collections. Hungarian, whereas the opposite holds true for the descending contour types.
Fig. 5. Number of melodic phrases mapped to each cell of the SOM of Fig. 5 fo both collections. Hungarian, whereas the opposite holds true for the descending contour types.
Fig. 6. (a) The proportion of melodies in minor mode in different regions of Fin- land. The red colour denotes a high proportion and the blue colour a low propor- tion. (b) The proportion of melodies starting with a tonic. If a musical database contains precise information about the geographical origin of each musical piece, geographical variation of musical features can be studied by applying methods of spatial estimation. Aarden and Huron (2001) created visualizations of the geographical variation of vari- ous musical features in the Essen collection. The Digital Archive of Fin- nish Folk Music contains detailed geographical information about the ori- gin of each tune. Fig. 6 shows visualizations obtained using this information and kernel density estimation.
Fig. 6. (a) The proportion of melodies in minor mode in different regions of Fin- land. The red colour denotes a high proportion and the blue colour a low propor- tion. (b) The proportion of melodies starting with a tonic. If a musical database contains precise information about the geographical origin of each musical piece, geographical variation of musical features can be studied by applying methods of spatial estimation. Aarden and Huron (2001) created visualizations of the geographical variation of vari- ous musical features in the Essen collection. The Digital Archive of Fin- nish Folk Music contains detailed geographical information about the ori- gin of each tune. Fig. 6 shows visualizations obtained using this information and kernel density estimation.

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References (26)

  1. Aarden B, Huron D (2001) Mapping European folksong: Geographical localiza- tion of musical features. Computing in Musicology 12:169-183
  2. Barlow SH, Morgenstern S (1948) A Dictionary of Musical Themes. Crown Pub- lishers, New York
  3. Brown JC (1993) Determination of meter of musical scores by autocorrelation. Journal of the Acoustical Society of America 94:1953-1957
  4. Cooper M, Foote J (2002) Automatic Music Summarization via Similarity Analy- sis. In Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR 2002), pp. 81-5
  5. Dixon S, Pampalk E, Widmer G (2003) Classification of dance music by periodic- ity patterns. Proceedings of the 4th International Conference on Music Infor- mation Retrieval (ISMIR'03), pp. 159-165
  6. Dowling WJ (1971) Recognition of inversions of melodies and melodic contours. Perception & Psychophysics 9:348-349
  7. Eerola T, Toiviainen P (2001) A method for comparative analysis of folk music based on musical feature extraction and neural networks. In: H Lappalainen (ed) Proceedings of the VII International Symposium of Systematic and Comparative Musicology and the III International Conference on Cognitive Musicology. University of Jyväskylä
  8. Eerola T, Toiviainen P (2004a) MIDI toolbox: MATLAB tools for music research. University of Jyväskylä, available at: http://wwwjyufi/musica/miditoolbox
  9. Eerola T, Toiviainen P (2004b) The Digital Archive of Finnish Folk Tunes Jyväskylä: University of Jyväskylä, available at: http://wwwjyufi/musica/sks
  10. Friedman JH (1987) Exploratory projection pursuit. Journal of the American Sta- tistical Association 82(397):249-266
  11. Goto M, Hashiguchi H, Nishimura T, Oka R (2002) RWC Music Database: Popu- lar Classical and Jazz Music Databases. In Proceedings of the 3rd Interna- tional Conference on Music Information Retrieval (ISMIR 2002), pp. 287-288
  12. Huron D (1995) The Humdrum Toolkit: Reference Manual. Center for Computer Assisted Research in the Humanities, Menlo Park, CA
  13. Huron D (1996) The melodic arch in Western folksongs. Computing in Musicol- ogy 10:3-23
  14. Hyvärinen A, Karhunen J, Oja E (2001) Independent Component Analysis. John Wiley & Sons, New York
  15. Juhász Z (2000) Contour analysis of Hungarian folk music in a multidimensional metric-space. Journal of New Music Research 29(1):71-83
  16. Klapuri A (2005) Automatic music transcription as we know it today. Journal of New Music Research 33(3):269-282
  17. Kohonen T (1995) Self-organizing maps. Springer-Verlag, Berlin Leman M, Lesaffre M, Tanghe K (2000) The IPEM toolbox manual. University of Ghent, IPEM
  18. Marillier CG (1983) Computer assisted analysis of tonal structure in the classical symphony. Haydn Yearbook 14:187-199
  19. Pampalk E, Dixon S, Widmer G (2004) Exploring music collections by browsing different views. Computer Music Journal 28(2):49-62
  20. Ponce de León PJ, Pérez-Sancho C, Iñesta J M (2004) A shallow description framework for music style recognition. Lecture Notes in Computer Science 3138:876-884
  21. RISM (1997) Répertoire international des sources musicales: International inven- tory of musical sources. In Series A/II Music manuscripts after 1600 [CD- ROM database].
  22. K. G. Saur Verlag, Munich
  23. Schaffrath H (1995) The Essen folksong collection in kern format [computer data- base]. Edited by D Huron. Center for Computer Assisted Research in the Hu- manities, Menlo Park, CA
  24. Silverman BW (1986) Density Estimation for Statistics and Data Analysis. Chap- man and Hall, London
  25. Toiviainen P, Eerola T (2006) Autocorrelation in meter induction: The role of ac- cent structure. Journal of the Acoustical Society of America 119(2):1164- 1170
  26. Vos PG, Troost JM (1989) Ascending and descending melodic intervals: statisti- cal findings and their perceptual relevance. Music Perception 6(4):383-396

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