Low frequency signal classification using clustering methods

D.V. Rahozin, A.Yu. Doroshenko

Abstract


The article considers the problem of low frequency signal classification, as sound or vibration pattern footprints may describe types of objects very well. In cases of a priori absence of object signal pattern information, the unsupervised learning methods based on clustering looks good enough for classification, and outperform neural net-based methods in case of limited power envelope. We have used big real-world sound and vibration data set to check several clustering methods (K-Means, OPTICS) for classification without any a priori data and have got good enough results. The article considers data set preparations including primary signal processing and the parameters to select appropriate clustering algorithms, which depends on input data shape. There are several examples of data classification, also cascaded methods for data set improvement are considered. Finally, we provide a good and practical guide for exploring low frequency signals using clustering methods, which can be used for real world observations and analysis for open space and inside buildings.

Prombles in programming 2024; 1: 48-56



Keywords


data classification; unsupervised learning; data clustering; sound processing

References


F. Meneghello, N. Dal Fabbro, D. Garlisi, I. Tinnirello, M. Rossi. A CSI Dataset for Wireless Human Sensing on 80 MHz Wi-Fi Channels. IEEE Communications Magazine, 2023. https://doi.org/10.48550/arXiv.2305.03170

ISO/IEC 7498-1:1994 Information technology — Open Systems Interconnection — Basic Reference Model: The Basic Model. June 1999.

Liu, Y., Li, J. (2023). A Survey of Spectrum Sensing Algorithms Based on Machine Learning. // In Proc. Xiong, N., Li, M., Li, K., Xiao, Z., Liao, L., Wang, L. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 153. Springer, Cham. https://doi.org/10.1007/978-3-031-20738-9_97

M. Salehi, H. Mirzaei, D. Hendrycks, Y. Li, M. H. Rohban, and M. Sabokrou, “A unified survey on anomaly, novelty, open-set, and out of-distribution detection: Solutions and future challenges,” arXiv preprint arXiv:2110.14051, 2021. https://arxiv.org/pdf/2110.14051.pdf

J. Hao, T. Ho Machine Learning Made Easy: A Review of Scikit-learn Package in Python Programming Language. // Journal of Educational and Behavioral Statistics. Vol 44, Feb 2019.

Doi: 10.3102/1076998619832248.

N. Alamdari, N. Kehtarnavaz. A Real-Time Smartphone App for Unsupervised Noise Classification in Realistic Audio Environments. // In Proc. IEEE Intl. Conf. on Consumer Electronics (ICCE), Jan 2019. 1-5. Doi: 10.1109/ICCE.2019.8662052.

D. Sculley. Web-scale k-means clustering. // In Proc. of 19th Intl. Conf. on World Wide Web, Apr. 2010. pp. 1177-1178. Doi: 10.1145/1772690.1772862.

J.L. Bentley. Multidimensional binary search trees used for associative searching. // Communications of the ACM. (1975) 18 (9): pp. 509–517. doi:10.1145/361002.361007.

D. Comaniciu, P. Meer. Mean shift: a robust approach toward feature space analysis // In Proc. IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, May 2002, doi: 10.1109/34.1000236.

J.H. Ward, Jr. Hierarchical Grouping to Optimize an Objective Function, // In Journal of the American Statistical Association, 1963, vol 58, pp. 236–244.

M. Ester, H. P. Kriegel, J. Sander, X. Xu. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise // In Proc. of the 2nd Inter. Conf. on Knowledge Discovery and Data Mining, 1996, pp. 226–231.

M. Ankerst, M.M. Breunig, H.P. Kriegel, J. Sander. OPTICS: ordering points to identify the clustering structure. // In ACM Sigmod Record, 1999, vol. 28, No. 2, pp. 49-60.

T. Zhang, R. Ramakrishnan, M. Livny. BIRCH: An efficient data clustering method for large databases. // In ACM Sigmod Record, 1996, vol. 25, issue 2, pp. 103-114.


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