A quantitative study on the influence of fingering variations on acoustic characteristics in guzheng performance
Main Article Content
Abstract
Guzheng timbre and expressive depth are closely determined by fingering techniques, since differences in string contact method, force direction, contact angle, and contact duration directly affect the time-domain and frequency-domain characteristics of the acoustic signal. However, traditional guzheng teaching and performance practice mainly rely on auditory experience rather than systematic acoustic evidence. This study focuses on four basic and widely used fingering techniques, namely gou, tuo, mo, and cuo. Under controlled conditions involving the same performer, instrument, pitch, and dynamic range, high-precision recordings are obtained in a professional recording environment. A total of 100 valid samples are analyzed, with 25 samples for each fingering technique. Acoustic features including energy envelope, attack time, decay time, spectral centroid, spectral roll-off, harmonic-to-noise ratio, and frequency-band energy distribution are extracted and analyzed using statistical tests and unsupervised clustering. Results show significant differences among the four techniques. The cuo technique presents the highest energy concentration, spectral centroid, and harmonic-to-noise ratio, while the mo technique shows broader frequency-band distribution, longer decay time, and lower harmonic-to-noise ratio. Clustering results confirm that the four fingering techniques are distinguishable in multidimensional acoustic feature space.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
I. Zinkiv and J. Ren, “Chinese zither se in ancient and traditional musical and instrumental cultures of Turkish, Mongolian and Tunguska-Manchu ethnic groups,” Yegah Müzikoloji Dergisi, vol. 7, no. 4, pp. 510-533, 2024, DOI: 10.51576/ymd.1543588.
A. Jamal S A and S. Bahar A A, “Entertainment Juncture of Two Castes: The identity and representation of Guqin and Guzheng,” Environment-Behaviour Proceedings Journal, vol. 7, no. SI9, pp. 445-449, 2022, DOI: 10.21834/ebpj.v7iSI9.4294.
D. Ackermann, F. Brinkmann, and S. Weinzierl, “Musical instruments as dynamic sound sources,” The Journal of the Acoustical Society of America, vol. 155, no. 4, pp. 2302-2313, 2024, DOI: 10.1121/10.0025463.
M. Casazza, F. Barone, E. Bonisoli, et al., “A procedure for the characterization of a music instrument vibro-acoustic fingerprint: The case of a contemporary violin,” Acta IMEKO, vol. 12, no. 3, pp. 1-6, 2023, DOI: 10.21014/actaimeko.v12i3.1445.
M. Nadirova and H. Aliyeva, “Evolution of musical instruments: from ancient times to modern innovations,” Norwegian Journal of development of the International Science, vol. 1, no. 135, pp. 12-16, 2024, DOI: 10.5281/zenodo.12578468.
Y. Gonzalez and C. Prati R, “Similarity of musical timbres using FFT-acoustic descriptor analysis and machine learning,” Eng, vol. 4, no. 1, pp. 555-568, 2023, DOI: 10.3390/eng4010033.
S. McAdams, E. Thoret, G. Wang, et al., “Timbral cues for learning to generalize musical instrument identity across pitch register,” The Journal of the Acoustical Society of America, vol. 153, no. 2, pp. 797-811, 2023, DOI: 10.1121/10.0017100.
S. Hamdan, R. Rahman M, S. Zainal Abidin A, et al., “Study on vibro-acoustic characteristics of bamboo-based angklung instrument,” BioResources, vol. 17, no. 1, pp. 1670-1679, 2022, DOI: 10.15376/biores.17.1.1670-1679.
M. Quintavalla, F. Gabrielli, and C. Canevari, “The acoustics of traditional Italian mandolins and their relation with soundboard wood properties,” International Journal of Wood Culture, vol. 2, no. 1-3, pp. 1-18, 2022, DOI: 10.1163/27723194-bja10001.
R. Bhagyalakshmi and B. Anandaraju M, “Identification of specific Musical instruments using Machine Learning models,” Journal of Integrated Science and Technology, vol. 13, no. 5, pp. 1108-1108, 2025, DOI: 10.62110/sciencein.jist.2025.v13.1108.
E. Evellyn and M. Sagala J, “Teknik Permainan Guzheng pada Turkish March Mozart Orkestrasi Quartet Wang Zhong Shan,” PROMUSIKA, vol. 12, no. 1, pp. 32-43, 2024, DOI: 10.24821/promusika.v12i1.10352.
P. Vogt and J. Kuhn, “What gives musical instruments their sound?,” The Physics Teacher, vol. 61, no. 1, pp. 80-81, 2023, DOI: 10.1119/5.0136722.
V. Rennoll, I. McLane, A. Eisape, et al., “Project-based learning through sensor characterization in a musical acoustics course,” The Journal of the Acoustical Society of America, vol. 152, no. 3, pp. 1932-1941, 2022, DOI: 10.1121/10.0014171.
M. Giri G A V and L. Radhitya M, “Musical instrument classification using audio features and convolutional neural network,” Journal of Applied Informatics and Computing, vol. 8, no. 1, pp. 226-234, 2024, DOI: 10.30871/jaic.v8i1.8058.