Tuning Systems of Traditional Georgian Singing Determined from a New Corpus of Field Recordings

Yıl 2022, Cilt: 6 Sayı: 2, 142 - 168, 31.12.2022

Frank Scherbaum Nana Mzhavanadze Sebastian Rosenzweig Meinard Müller

https://doi.org/10.33906/musicologist.1068947

Cited By: 1

Öz

In this study we examine the tonal organization of the 2016 GVM dataset, a newly-created corpus of high-quality multimedia field recordings of traditional Georgian singing with a focus on Svaneti. For this purpose, we developed a new processing pipeline for the computational analysis of non-western polyphonic music which was subsequently applied to the complete 2016 GVM dataset. To evaluate under what conditions a single tuning system is representative of current Svan performance practice, we examined the stability of the obtained tuning systems from an ensemble-, a song-, and a corpus-related perspective. Furthermore, we compared the resulting Svan tuning systems with the tuning systems obtained for the Erkomaishvili dataset (Rosenzweig et al., 2020) in the study by Scherbaum et al. (2020). In comparison to a 12-TET (12-tone-equal- temperament) system, the Erkomaishvili and the Svan tuning systems are surprisingly similar. Both systems show a strong presence of pure fourths (500 cents) and fifths (700 cents), and ‘neutral’ thirds (peaking around 350 cents) as well as ‘neutral’ sixths. In addition, the sizes of the melodic and the harmonic seconds in both tuning systems differ systematically from each other, with the size of the harmonic second being systematically larger than the melodic one.

Anahtar Kelimeler

Traditional Georgian Music, Tuning, Computational ethnomusicology

Destekleyen Kurum

German Research Foundation

Proje Numarası

DFG MU 2686/13-1, SCHE 280/20-1] (2018 - 2022)

Kaynakça

• Ambrazevičius, Rytis, Budrys, Robertas, & Viśnievska, Irena (2015). Scales in Lithuanian traditional music: acoustics, cognition, and contexts. Kaunas: Kaunas University of Technology.
• Gómez, Emilia, Herrera, Perfecto, & Gómez-Martin, Francisco (2013). “Computational Ethnomusicology: perspectives and challenges” Journal of New Music Research, 42(2): 111–112.
• Heller, Eric J. (2012). “Pitch Perception”. In Why You Hear What You Hear: An Experiential Approach to Sound, Music, and Psychoacoustics, pp. 437 - 479. Princeton: Princeton University Press.
• Jordania, Joseph (2006). Who asked the first question? The origins of human choral singing, intelligence, language and speech. Tbilisi: Logos.
• Mauch, Matthias, Cannam, Chris, Bittner, Rachel, Fazekas, Giörgy, Salamon, Justin, Dai, Jiajie, Bello, Juan, Dixon, Simon (2015). Computer-aided Melody Note Transcription Using the Tony Software: Accuracy and Efficiency. Proceedings of the First International Conference on Technologies for Music Notation and Representation (TENOR), Paris, France. Retrieved from http://www.tenor-conference.org/proceedings/2015/04-Mauch-Tony.pdf Last accessed 2022/12/09
• Mauch, Matthias, & Dixon, Simon (2014). PYIN: A fundamental frequency estimator using probabilistic threshold distributions. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence/Italy. (pp. 659-663). Retrieved from https://ieeexplore.ieee.org/document/6853678 Last accessed 2022/12/09
• Müller, Meinard, Rosenzweig, Sebastian, Driedger, Jonathan, & Scherbaum, Frank (2017). Interactive Fundamental Frequency Estimation with Applications to Ethnomusicological Research. Paper presented at Conference on Semantic Audio, Erlangen, Germany, 22 – 24 June 2017.
• Mzhavanadze, Nana (2018). “Svanuri sak’ult’o rit’ualis musik’ologiur-antrop’ologiuri asp’ekt’ebi” (Musicological and anthropological aspects of Svan sacred ritual). PhD Dissertation, Ilia State University, Tbilisi: Georgia
• Mzhavanadze, Nana, & Scherbaum, Frank (2020). “Svan Funeral Dirges (Zär): Musicological analysis” Musicologist, 4(2): 168–197.
• Rosenzweig, Sebastian, Scherbaum, Frank, & Meinard Müller. (2022). “Computer-Assisted Analysis of Field Recordings: A Case Study of Georgian Funeral Songs”. ACM Journal on Computing and Cultural Heritage (JOCCH). Retrieved from https://dl.acm.org/doi/abs/10.1145/3551645
• Rosenzweig, Sebastian, Scherbaum, Frank, & Müller, Meinard (2019). Detecting Stable Regions in Frequency Trajectories for Tonal Analysis of Traditional Georgian Vocal Music. Proceedings of the 20th International Society for Music Information Retrieval Conference.(Paper presented at 20th International Society for Music Information Retrieval Conference on November 4-8, 2019, Delft, The Netherlands) Flexer, Arthur; Peeters, Geoffroy; Urbano, Julian; Volk, Anjo (Eds.), (pp. 352–359). Retrieved from https://archives.ismir.net/ismir2019/paper/000041.pdf
• Rosenzweig, Sebastian, Scherbaum, Frank, Shugliashvili, David, Arifi-Müller, Vlora, & Müller, Meinard (2020). “Erkomaishvili Dataset: A Curated Corpus of Traditional Georgian Vocal Music for Computational Musicology” Transactions of the International Society for Music Information Retrieval, 3(1):31–41.
• Scherbaum, Frank, Müller, Meinard, & Rosenzweig, Sebastian (2017). Analysis of the Tbilisi State Conservatory Recordings of Artem Erkomaishvili in 1966. In 7th International Workshop on Folk Music Analysis, June 14-16 2017, Malaga, Spain (pp. 29–36). Retrieved from https://doi.org/doi.org/10.25932/publishup-47614
• Scherbaum, Frank, & Mzhavanadze, Nana (2020). Svan Funeral Dirges (Zär): Musical Acoustical Analysis of a New Collection of Field Recordings. Musicologist, 4(2), 138–167.
• Scherbaum, Frank, Mzhavanadze, Nana, Arom, Simha, Rosenzweig, Sebastian, & Müller, Meinard (2020). ”Tonal Organization of the Erkomaishvili Dataset: Pitches, Scales, Melodies and Harmonies” Computational Analysis of Traditional Georgian Vocal Music. Ed. Frank Scherbaum: pp. 64. Potsdam/Germany: Universitätsverlag Potsdam.
• Scherbaum, Frank, Mzhavanadze, Nana, & Dadunashvili, Elguja (2018). „A Web-Based, Long-Term Archive of Audio, Video, and Larynx-Microphone Field Recordings of Traditional Georgian Singing, Praying and Lamenting with Special Emphasis on Svaneti” Paper presented at 9. International Symposium on Traditional Polyphony, Tbilisi, Georgia, Organization date.
• Scherbaum, Frank, Mzhavanadze, Nana, Rosenzweig, Sebastian, & Müller, Meinard (2019). “Multi-media recordings of traditional Georgian vocal music for computational analysis”. Paper presented at the 9th International Workshop on Folk Music Analysis, Birmingham, England. 2 -4 July, 2019.
• Tzanetakis, George (2014). “Computational ethnomusicology: a music information retrieval perspective” Proceedings of the International Computer Music Conference ICMC|SMC|2014, 14-20 September 2014, Athens/Greece, pp. 69–74.
• Tzanetakis, George, Kapur, Ajay, Schloss, Walter Andrew Schloss, & Wright, Mathew (2007). “Computational Ethnomusicology” Journal of Interdisciplinary Music Studies, 1(2): 1–24.
• Wolfram, Stephen. (2016). How to teach computational thinking. Blogpost “Stephen Wolfram Writings”, Retrieved from https://writings.stephenwolfram.com/2016/09/how-to-teach-computational-thinking/ (Last accessed 2022/09/15).