Classification of Ethnic Music Styles Combining Timbre Clustering and Rhythmic Pattern Matching
Main Article Content
Abstract
Current research on ethnic music style classification faces challenges related to timbre overlap in multi-instrument recordings and unstable representation of rhythmic structures, which reduce the reliability of style discrimination. This paper proposes a classification framework combining timbre clustering with rhythmic pattern matching. Robust spectral–temporal feature representation and sequential pattern analysis are increasingly important in intelligent acoustic sensing and electromagnetic signal processing applications, providing methodological support for complex signal interpretation across engineering domains. The proposed framework constructs independent yet complementary representations for timbre distribution and rhythm organization, enabling spectral and temporal ambiguities to be jointly constrained within a unified classification process. Spectral features are extracted using short-time Fourier transform (STFT), followed by the computation of 13-dimensional MFCCs, first-order differential coefficients, spectral centroids, roll-off rates, and spectral flux features. After principal component analysis, Gaussian mixture models are employed for timbre clustering to isolate instrument-related acoustic components. Rhythm features are subsequently generated from timbre-cluster-dependent energy envelopes and aligned with ethnic rhythm templates through Dynamic Time Warping, allowing robust similarity measurement under nonlinear temporal variations. The timbre cluster probability distribution and rhythm matching descriptors are fused into a multidimensional feature vector and classified using a support vector machine. Experimental results demonstrate that the proposed model achieves an overall accuracy of 90.9% and an average F1 score of 91.3% for six ethnic music styles, validating the effectiveness and stability of jointly modeling timbre clustering and rhythmic pattern matching. The proposed framework provides a reliable strategy for complex spectral–temporal signal analysis and offers potential methodological insights for advanced electromagnetic sensing and intelligent signal interpretation.
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
L. Liu and H. Liang, “Analysis of the Style Characteristics of Regional Folk Songs and Music Classification Algorithms,” Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 29, no. 1, pp. 33-40, 2025, doi: 10.20965/jaciii.2025.p0033.
L. Xie, Y. Wang, and Y. Gao, “Acoustical feature analysis and optimization for aesthetic recognition of Chinese traditional music,” EURASIP Journal on Audio, Speech, and Music Processing, vol. 2024, no. 1, pp. 7-20, 2024, doi: 10.1186/s13636-023-00326-2.
X. Wang, L. Wang, and L. Xie, “Comparison and analysis of acoustic features of western and Chinese classical music emotion recognition based on VA model,” Applied Sciences, vol. 12, no. 12, pp. 5787-5804, 2022, doi: 10.3390/app12125787.
R. Pandeya Y and J. Lee, “Deep learning-based late fusion of multimodal information for emotion classification of music video,” Multimedia Tools and Applications, vol. 80, no. 2, pp. 2887-2905, 2021, doi: 10.1007/s11042-020-08836-3.
Q. Yue, L. Wang, and J. Luo, “Pattern Recognition Based Music Style Recognition and Teaching Application in Higher Education Music Education,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, pp. 1-12, 2024, doi: 10.2478/amns-2024-3474.
H. Xue, C. Sun, M. Tang, C. Hu, Z. Yuan, M. Huang, et al., “Effective acoustic parameters for automatic classification of performed and synthesized Guzheng music,” EURASIP Journal on Audio, Speech, and Music Processing, vol. 2023, no. 1, pp. 50-62, 2023, doi: 10.1186/s13636-023-00320-8.
M. Chen, “Application of Deep Learning in Music Genre Classification,” Frontiers in Artificial Intelligence Research, vol. 2, no. 3, pp. 248-260, 2025, doi: 10.71465/fair331.
Y. Zhang and T. Li, “Music genre classification with parallel convolutional neural networks and capuchin search algorithm,” Scientific Reports, vol. 15, no. 1, pp. 9580-9597, 2025, doi: 10.1038/s41598-025-90619-7.
K. Zemlianova, A. Bose, and J. Rinzel, “Dynamical mechanisms of how an RNN keeps a beat, uncovered with a low-dimensional reduced model,” Scientific reports, vol. 14, no. 1, pp. 26388-26401, 2024, doi: 10.1038/s41598-024-77849-x.
W. Large E, I. Roman, C. Kim J, J. Cannon, J. Pazdera, L. Trainor, et al., “Dynamic models for musical rhythm perception and coordination,” Frontiers in Computational Neuroscience, vol. 17, no. 1, pp. 1151895-1151907, 2023, doi: 10.3389/fncom.2023.1151895.
K. Gourisaria M, R. Agrawal, M. Sahni, and P. Singh, “Comparative analysis of audio classification with MFCC and STFT features using machine learning techniques,” Discover Internet of Things, vol. 4, no. 1, pp. 1-24, 2024, doi: 10.1007/s43926-023-00049-y.
E. Setiorini, M. Widjaja, and A. Wicaksana, “Reduced Convolutional Recurrent Neural Network Using MFCC for Music Genre Classification on the GTZAN Dataset,” Informatica, vol. 49, no. 17, pp. 1-10, 2025, doi: 10.31449/inf.v49i17.6885.
K. Schulze-Forster, G. Richard, L. Kelley, C. Doire, and R. Badeau, “Unsupervised music source separation using differentiable parametric source models,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 31, no. 1, pp. 1276-1289, 2023, doi: 10.1109/TASLP.2023.3252272.
G. Zhu, J. Darefsky, F. Jiang, A. Selitskiy, and Z. Duan, “Music source separation with generative flow,” IEEE Signal Processing Letters, vol. 29, no. 1, pp. 2288-2292, 2022, doi: 10.1109/LSP.2022.3219355.
C. Cavicchia, M. Vichi, and G. Zaccaria, “Parsimonious ultrametric Gaussian mixture models,” Statistics and Computing, vol. 34, no. 3, pp. 108-131, 2024, doi: 10.1007/s11222-024-10405-9.
K. Sreekar and D. Reddy A, “Musical tones classification using machine learning,” International Journal for Research in Applied Science and Engineering Technology, vol. 10, no. 12, pp. 1004-1007, 2022, doi: 10.22214/ijraset.2022.48084.
Seo Jin Soo, “Brief Paper: Salient Chromagram Extraction Based on the Savitzky-Golay Filter for Cover Song Identification,” J Multimed Inf Syst, vol. 9, no. 1, pp. 69-72, 2022, doi: 10.33851/JMIS.2022.9.1.69.
X. Chen, Y. Guo, and J. Na, “Encoder signal-based optimized Savitzky-Golay and adaptive spectrum editing for feature extraction of rolling element bearing under low-speed and variable-speed conditions,” ISA transactions, vol. 154, no. 1, pp. 371-388, 2024, doi: 10.1016/j.isatra.2024.07.034.
D. Jiang, “Matching model of dance movements and music rhythm features using human posture estimation,” Computational Intelligence and Neuroscience, vol. 2022, no. 1, pp. 7331210-7331222, 2022, doi: 10.1155/2022/7331210.
J. Kraprayoon, A. Pham, and J. Tsai T, “Improving the robustness of DTW to global time warping conditions in audio synchronization,” Applied Sciences, vol. 14, no. 4, pp. 1459-1476, 2024, doi: 10.3390/app14041459.
J. Stübinger and D. Walter, “Using multi-dimensional dynamic time warping to identify time-varying lead-lag relationships,” Sensors, vol. 22, no. 18, pp. 6884-6899, 2022, doi: 10.3390/s22186884.
Y. Xia and F. Xu, “Study on music emotion recognition based on the machine learning model clustering algorithm,” Mathematical Problems in Engineering, vol. 2022, no. 1, pp. 9256586-9256598, 2022, doi: 10.1155/2022/9256586.
S. Hızlısoy, S. Arslan R, and E. Çolakoğlu, “Music Genre Recognition Based on Hybrid Feature Vector with Machine Learning Methods,” Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, vol. 38, no. 3, pp. 739-750, 2023, doi: 10.21605/cukurovaumfd.1377737.