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Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology Cover

Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology

Open Access
|Apr 2020

References

  1. 1Arzt, A. (2016). Flexible and robust music tracking. PhD thesis, Johannes Kepler Universität Linz.
  2. 2Böck, S., Krebs, F., & Schedl, M. (2012). Evaluating the online capabilities of onset detection methods. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 4954.
  3. 3Boersma, P. (2001). Praat, a system for doing phonetics by computer. Glot International, 5(9/10), 341345.
  4. 4Bogdanov, D., Wack, N., Gómez, E., Gulati, S., Herrera, P., Mayor, O., Roma, G., Salamon, J., Zapata, J. R., & Serra, X. (2013). Essentia: An audio analysis library for music information retrieval. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 493498. Curitiba, Brazil. DOI: 10.1145/2502081.2502229
  5. 5Byrd, D., & Simonsen, J. G. (2015). Towards a standard testbed for optical music recognition: Definitions, metrics, and page images. Journal of New Music Research, 44(3), 169195. DOI: 10.1080/09298215.2015.1045424
  6. 6Cannam, C., Landone, C., & Sandler, M. B. (2010). Sonic Visualiser: An open source application for viewing, analysing, and annotating music audio files. In Proceedings of the International Conference on Multimedia, pages 14671468. Florence, Italy. DOI: 10.1145/1873951.1874248
  7. 7Cano, E., FitzGerald, D., Liutkus, A., Plumbley, M. D., & Stöter, F. (2019). Musical source separation: An introduction. IEEE Signal Processing Magazine, 36(1), 3140. DOI: 10.1109/MSP.2018.2874719
  8. 8Chokhonelidze, E. (2010). Some characteristic features of the voice coordination and harmony in Georgian multipart singing. In Echoes from Georgia: Seventeen Arguments on Georgian Polyphony, pages 135145. Nova Science Publishers.
  9. 9Cuesta, H., Gómez, E., Martorell, A., & Loáiciga, F. (2018). Analysis of intonation in unison choir singing. In Proceedings of the International Conference of Music Perception and Cognition (ICMPC), pages 125130. Graz, Austria.
  10. 10Cuthbert, 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 (ISMIR), pages 637642. Utrecht, The Netherlands.
  11. 11Dzhambazov, G., Srinivasamurthy, A., Sentürk, S., & Serra, X. (2016). On the use of note onsets for improved lyrics-to-audio alignment in Turkish makam music. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 716722. New York City, USA.
  12. 12Erkvanidze, M. (2016). The Georgian musical system. In Proceedings of the International Workshop on Folk Music Analysis, pages 7479. Dublin, Ireland.
  13. 13Ganguli, K. K., & Rao, P. (2018). On the distributional representation of ragas: Experiments with allied raga pairs. Transactions of the International Society for Music Information Retrieval (TISMIR), 1(1), 7995. DOI: 10.5334/tismir.11
  14. 14Gasser, M., Arzt, A., Gadermaier, T., Grachten, M., & Widmer, G. (2015). Classical music on the web – user interfaces and data representations. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), pages 571577. Málaga, Spain.
  15. 15Godsill, S., Rayner, P., & Cappé, O. (2002). Digital audio restoration. In Applications of Digital Signal Processing to Audio and Acoustics, pages 133194. Springer. DOI: 10.1007/0-306-47042-X_4
  16. 16Gómez, E., Herrera, P., & Gómez-Martin, F. (2013). Computational ethnomusicology: Perspectives and challenges. Journal of New Music Research, 42(2), 111112. DOI: 10.1080/09298215.2013.818038
  17. 17Gong, R., Repetto, R. C., & Serra, X. (2017). Creating an a cappella singing audio dataset for automatic jingju singing evaluation research. In Proceedings of the International Workshop on Digital Libraries for Musicology, pages 3740. DOI: 10.1145/3144749.3144757
  18. 18Graham, J. (2015). The transcription and transmission of Georgian Liturgical Chant. PhD thesis, Princeton University.
  19. 19Jeong, D., Kwon, T., Park, C., & Nam, J. (2017). PerformScore: Toward performance visualization with the score on the web browser. In Demos and Late Breaking News of the International Society for Music Information Retrieval Conference (ISMIR), Suzhou, China.
  20. 20Jgharkava, I. (2016). Pearls of Georgian Chant. CD. Produced by the Georgian Chanting Foundation & Tbilisi State Conservatoire.
  21. 21Kroher, N., Díaz-Báñez, J. M., Mora, J., & Gómez, E. (2016). Corpus COFLA: A research corpus for the computational study of flamenco music. Journal on Computing and Cultural Heritage (JOCCH), 9(2), 10:110:21. DOI: 10.1145/2875428
  22. 22Lartillot, O., & Toiviainen, P. (2007). MIR in MATLAB (II): A toolbox for musical feature extraction from audio. In Proceedings of the International Conference on Music Information Retrieval (ISMIR), pages 127130. Vienna, Austria.
  23. 23McFee, B., Raffel, C., Liang, D., Ellis, D. P., McVicar, M., Battenberg, E., & Nieto, O. (2015). Librosa: Audio and music signal analysis in python. In Proceedings of the Python Science Conference, pages 1825. DOI: 10.25080/Majora-7b98e3ed-003
  24. 24Müller, M., Arzt, A., Balke, S., Dorfer, M., & Widmer, G. (2019). Cross-modal music retrieval and applications: An overview of key methodologies. IEEE Signal Processing Magazine, 36(1), 5262. DOI: 10.1109/MSP.2018.2868887
  25. 25Müller, M., Grosche, P., & Wiering, F. (2009). Robust segmentation and annotation of folk song recordings. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 735740. Kobe, Japan.
  26. 26Müller, M., Rosenzweig, S., Driedger, J., & Scherbaum, F. (2017). Interactive fundamental frequency estimation with applications to ethnomusicological research. In Proceedings of the AES International Conference on Semantic Audio, pages 186193. Erlangen, Germany.
  27. 27Müller, M., & Zalkow, F. (2019). FMP notebooks: Educational material for teaching and learning fundamentals of music processing. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), Delft, The Netherlands.
  28. 28Panteli, M. (2018). Computational analysis of world music corpora. PhD thesis, Queen Mary University of London, UK.
  29. 29Pugin, L., Zitellini, R., & Roland, P. (2014). Verovio: A library for engraving MEI music notation into SVG. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 107112. Taipei, Taiwan.
  30. 30Rebelo, A., Fujinaga, I., Paszkiewicz, F., Marcal, A. R. S., Guedes, C., & Cardoso, J. S. (2012). Optical music recognition: State-of-the-art and open issues. International Journal of Multimedia Information Retrieval, 1(3), 173190. DOI: 10.1007/s13735-012-0004-6
  31. 31Repetto, R. C., Pretto, N., Chaachoo, A., Bozkurt, B., & Serra, X. (2018). An open corpus for the computational research of Arab-Andalusian music. In Proceedings of the International Conference on Digital Libraries for Musicology, pages 7886. Paris, France. DOI: 10.1145/3273024.3273025
  32. 32Repetto, R. C., & Serra, X. (2014). Creating a corpus of Jingju (Beijing Opera) music and possibilities for melodic analysis. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 313318. Taipei, Taiwan.
  33. 33Rosenzweig, S. (2017). Audio processing techniques for analyzing Georgian vocal music. Master Thesis, Friedrich-Alexander-Universität Erlangen-Nürnberg.
  34. 34Rosenzweig, S., Scherbaum, F., & Müller, M. (2019). Detecting stable regions in frequency trajectories for tonal analysis of traditional Georgian vocal music. In Proceedings of the International Society for Music Information Retrieval Conference (ISMIR), pages 352359. Delft, The Netherlands.
  35. 35Röwenstrunk, D., Prätzlich, T., Betzwieser, T., Müller, M., Szwillus, G., & Veit, J. (2015). Das Gesamtkunstwerk Oper aus Datensicht – Aspekte des Umgangs mit einer heterogenen Datenlage im BMBF-Projekt “Freischütz Digital”. Datenbank-Spektrum, 15(1), 6572. DOI: 10.1007/s13222-015-0179-0
  36. 36Salamon, J., & Gómez, E. (2012). Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Transactions on Audio, Speech, and Language Processing, 20(6), 17591770. DOI: 10.1109/TASL.2012.2188515
  37. 37Salamon, J., Gómez, E., Ellis, D. P. W., & Richard, G. (2014). Melody extraction from polyphonic music signals: Approaches, applications, and challenges. IEEE Signal Processing Magazine, 31(2), 118134. DOI: 10.1109/MSP.2013.2271648
  38. 38Scherbaum, F. (2016). On the benefit of larynxmicrophone field recordings for the documentation and analysis of polyphonic vocal music. Proceedings of the International Workshop Folk Music Analysis, pages 8087.
  39. 39Scherbaum, F., Müller, M., & Rosenzweig, S. (2017). Analysis of the Tbilisi State Conservatory recordings of Artem Erkomaishvili in 1966. In Proceedings of the International Workshop on Folk Music Analysis, pages 2936. Málaga, Spain.
  40. 40Scherbaum, F., Mzhavanadze, N., Rosenzweig, S., & Müller, M. (2019). Multi-media recordings of traditional Georgian vocal music for computational analysis. In Proceedings of the International Workshop on Folk Music Analysis, pages 16. Birmingham, UK.
  41. 41Şentürk, S. (2016). Computational analysis of audio recordings and music scores for the description and discovery of Ottoman-Turkish Makam music. PhD thesis, Universitat Pompeu Fabra.
  42. 42Serra, X. (2014a). Computational approaches to the art music traditions of India and Turkey. Journal of New Music Research, Special Issue on Computational Approaches to the Art Music Traditions of India and Turkey, 43(1), 12. DOI: 10.1080/09298215.2014.894083
  43. 43Serra, X. (2014b). Creating research corpora for the computational study of music: The case of the CompMusic project. In Proceedings of the AES International Conference on Semantic Audio, London, UK.
  44. 44Shugliashvili, D. (2014). Georgian Church Hymns, Shemokmedi School. Georgian Chanting Foundation.
  45. 45Six, J., Cornelis, O., & Leman, M. (2013). Tarsos, a modular platform for precise pitch analysis of Western and non-Western music. Journal of New Music Research, 42(2), 113129. DOI: 10.1080/09298215.2013.797999
  46. 46Srinivasamurthy, A., Koduri, G. K., Gulati, S., Ishwar, V., & Serra, X. (2014). Corpora for music information research in Indian art music. In Proceedings of the Joint Conference 40th International Computer Music Conference (ICMC) and 11th Sound and Music Computing Conference (SMC), Athens, Greece.
  47. 47Thomas, V., Fremerey, C., Müller, M., & Clausen, M. (2012). Linking sheet music and audio – challenges and new approaches. In Müller, M., Goto, M., & Schedl, M., Editors, Multimodal Music Processing, volume 3 of Dagstuhl Follow-Ups, pages 122. Schloss Dagstuhl–Leibniz-Zentrum für Informatik, Dagstuhl, Germany.
  48. 48Tsereteli, Z., & Veshapidze, L. (2014). On the Georgian traditional scale. In Proceedings of the International Symposium on Traditional Polyphony, pages 288295. Tbilisi, Georgia.
  49. 49Tzanetakis, G. (2009). Music analysis, retrieval and synthesis of audio signals MARSYAS. In Proceedings of the ACM International Conference on Multimedia (ACM-MM), pages 931932. Vancouver, British Columbia, Canada. DOI: 10.1145/1631272.1631459
  50. 50Tzanetakis, G. (2014). Computational ethnomusicology: A music information retrieval perspective. In Proceedings of the Joint Conference 40th International Computer Music Conference (ICMC) and 11th Sound and Music Computing Conference (SMC), pages 6973. Athens, Greece.
  51. 51Tzanetakis, G., Kapur, A., Schloss, W. A., & Wright, M. (2007). Computational ethnomusicology. Journal of Interdisciplinary Music Studies, 1(2), 124.
  52. 52Uyar, B., Atli, H. S., Sentürk, S., Bozkurt, B., & Serra, X. (2014). A corpus for computational research of Turkish makam music. In Proceedings of the International Workshop on Digital Libraries for Musicology, pages 17. London, UK. DOI: 10.1145/2660168.2660174
  53. 53van Kranenburg, P., de Bruin, M., & Volk, A. (2019). Documenting a song culture: The Dutch Song Database as a resource for musicological research. International Journal on Digital Libraries, 20(1), 1323. DOI: 10.1007/s00799-017-0228-4
  54. 54Werner, N., Balke, S., Stöter, F.-R., Müller, M., & Edler, B. (2017). trackswitch.js: A versatile webbased audio player for presenting scientific results. In Proceedings of the Web Audio Conference (WAC), London, UK.
  55. 55Zalkow, F., Rosenzweig, S., Graulich, J., Dietz, L., Lemnaouar, E. M., & Müller, M. (2018). A web-based interface for score following and track switching in choral music. In Demos and Late Breaking News of the International Society for Music Information Retrieval Conference (ISMIR), Paris, France.
DOI: https://doi.org/10.5334/tismir.44 | Journal eISSN: 2514-3298
Language: English
Submitted on: Oct 21, 2019
Accepted on: Feb 3, 2020
Published on: Apr 10, 2020
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2020 Sebastian Rosenzweig, Frank Scherbaum, David Shugliashvili, Vlora Arifi-Müller, Meinard Müller, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.