Amplitude-Based Statistical Filtering Method for Resonance Localization in Low-Cost Acoustic Sensing Systems
DOI:
10.33395/sinkron.v10i3.16311Abstract
Acoustic resonance experiments using closed organ pipes are widely used to study the relationship between sound frequency, wavelength, and air-column length. However, low-cost sensor-based systems often produce unstable acoustic signals contaminated by environmental noise, amplitude fluctuations, and sensor-response variability, making resonance identification difficult. This study aims to develop and evaluate an amplitude-based statistical filtering method to improve signal stability and determine the fundamental resonance position more accurately. The proposed method was evaluated using a closed organ pipe experiment integrated with an acoustic sensor and microcontroller-based data acquisition system. Acoustic signals were processed using amplitude-based statistical filtering to extract dominant resonance responses and improve resonance localization. Statistical evaluation was conducted to analyze signal stability and measurement accuracy. The results showed that the filtering process reduced the standard deviation from 9.24 cm in the raw dataset to 6.72 cm in the final resonance candidates, indicating improved resonance localization stability. The experimental resonance length obtained after filtering was 16.12 cm, while the theoretical resonance length was 16.75 cm, resulting in a relative error of 3.76%. These findings demonstrate that the proposed filtering method can improve resonance detection accuracy using a simple, practical, and computationally efficient approach suitable for low-cost educational laboratory systems.
Downloads
References
Contreras, V., & Volke-Sepúlveda, K. (2024). Enhanced standing-wave acoustic levitation using high-order transverse modes in phased array ultrasonic cavities. Ultrasonics, 138(December 2023). https://doi.org/10.1016/j.ultras.2023.107230
Enkhbat, G., Xu, Y., Zhang, Y., & Xie, G. (2024). Acoustic Resonance Fast Detection Method of Harmonic Reducer Based on Support Vector Machine Algorithm. Journal of Donghua University (English Edition), 41(3), 289–297. https://doi.org/10.19884/j.1672-5220.202310003
Gidion, G., Aftab, T., Reindl, L. M., & Rupitsch, S. J. (2023). Resonance phenomena in dielectric media: A review and comparison of acoustic and electromagnetic modes. Journal of Advanced Dielectrics, 13(4), 1–6. https://doi.org/10.1142/S2010135X23410084
Guizzo, E., Marinoni, C., Pennese, M., Ren, X., Zheng, X., Zhang, C., Masiero, B., Uncini, A., & Comminiello, D. (2022). L3Das22 Challenge: Learning 3D Audio Sources in a Real Office Environment. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2022-May, 9186–9190. https://doi.org/10.1109/ICASSP43922.2022.9746872
Heriard-Dubreuil, B., Besson, A., Cohen-Bacrie, C., & Thiran, J. P. (2023). Interpolation-Based Regularization for Speed of Sound Estimation in Layered Media. IEEE International Ultrasonics Symposium, IUS. https://doi.org/10.1109/IUS51837.2023.10307144
Iskandar, F., & Pramudya, Y. (2024). A Comparative Study of Sound Resonance Using Arduino-Based Ultrasonic Sensors and Visualization Analysis with Python. Jurnal Materi Dan Pembelajaran Fisika, 14(2), 72. https://doi.org/10.20961/jmpf.v14i2.93454
Iskandar, F., & Pramudya, Y. (2025). Studi Faktor Kualitas Resonansi Bunyi pada Pipa Organa Tertutup Menggunakan Panjang Efektif. JIPFRI (Jurnal Inovasi Pendidikan Fisika Dan Riset Ilmiah), 9(1), 46–51. https://doi.org/10.30599/jipfri.v9i1.4186
Junior, R. P., Rocha, C. A. F. Da, Chang, B. S., & Le Ruyet, D. (2023). A Generalized Two-Dimensional FFT Precoded Filter Bank Scheme With Low Complexity Equalizers in Time-Frequency Domain. IEEE Access, 11(September), 112414–112428. https://doi.org/10.1109/ACCESS.2023.3323928
Karjadi, D. A., & Haryono, H. (2022). Objects Monitoring RADAR using Bluetooth Ultrasonic. SinkrOn, 7(2), 359–366. https://doi.org/10.33395/sinkron.v7i2.11347
Komarizadehasl, S., Mobaraki, B., Ma, H., Lozano-Galant, J. A., & Turmo, J. (2022). Low-Cost Sensors Accuracy Study and Enhancement Strategy. Applied Sciences (Switzerland), 12(6). https://doi.org/10.3390/app12063186
Lee, B., Yang, J., Cho, J. S., & Kim, S. (2022). A Low-Power Digital Capacitive MEMS Microphone Based on a Triple-Sampling Delta-Sigma ADC With Embedded Gain. IEEE Access, 10, 75323–75330. https://doi.org/10.1109/ACCESS.2022.3188661
Liu, Z., Chen, L., Zhou, X., Jiao, Z., Guo, G., & Chen, R. (2023). Machine Learning for Time-of-Arrival Estimation With 5G Signals in Indoor Positioning. IEEE Internet of Things Journal, 10(11), 9782–9795. https://doi.org/10.1109/JIOT.2023.3234123
Lupitha, M., & Haryono, H. (2022). Prototype of movement monitoring Objects using Arduino Nano and SMS Notifications. Sinkron, 7(2), 601–610. https://doi.org/10.33395/sinkron.v7i2.11413
Manela, A., & Ben-Ami, Y. (2024). Acoustic interaction of a finite body in a rarefied gas: Does sound reciprocity hold at non-continuum conditions? Journal of Fluid Mechanics, 999, 1–29. https://doi.org/10.1017/jfm.2024.1003
Neimarlija, N., & Arifović, K. (2023). Mathematical And Numerical Analysis Of The Relationship Among The Loschmidt Constant, The Avogadro Constant, And The Speed Of Sound In Real Gases At Different The Pvt Thermodynamic Properties. Mašinstvo, 20(1), 19–29. https://doi.org/10.62456/jmem.2023.01.019
Pawłowski, E., Szlachta, A., & Otomański, P. (2023). The Influence of Noise Level on the Value of Uncertainty in a Measurement System Containing an Analog-to-Digital Converter. Energies, 16(3). https://doi.org/10.3390/en16031060
Pratiwi, I., Nugroho, H. S., Putri, S. A., Chen, D., Suryani, R., & Al-Hakim, R. R. (2025). Cross-platform Data Visualization of Acoustic Phenomena in Open and Closed Pipe Resonance Using Python and Microsoft Excel. J. Aceh Phys. Soc, 14(4), 28–36. https://doi.org/10.24815/jacps.v14i4.47388
Ramesh, G., Logeshwaran, J., Gowri, J., & Mathew, A. (2022). the Management and Reduction of Digital Noise in Video Image Processing By Using Transmission Based Noise Elimination Scheme. Ictact Journal on Image and Video Processing, 9102(13), 1. https://doi.org/10.21917/ijivp.2022.0398
Rashid, Dr. K. M. J. (2023). Optimize the Taguchi method, the signal-to-noise ratio, and the sensitivity. International Journal of Statistics and Applied Mathematics, 8(6), 64–70. https://doi.org/10.22271/maths.2023.v8.i6a.1406
Sahin, M. A., Ali, M., Park, J., & Destgeer, G. (2023). Fundamentals of acoustic wave generation and propagation. In Acoustic Technologies in Biology and Medicine. https://doi.org/10.1002/9783527841325.ch1
Septiana, R., Deosa P. Caniago, & Harun Kurniawan. (2024). Evaluasi dan Kalibrasi Data Akuisisi Temperatur Berbasis Arduino dan MAX31855. Jurnal Elektronika Dan Otomasi Industri, 11(2), 621–627. https://doi.org/10.33795/elkolind.v11i2.5250
Singh, A. K., & Krishnan, S. (2023). ECG signal feature extraction trends in methods and applications. In BioMedical Engineering Online (Vol. 22, Number 1). BioMed Central. https://doi.org/10.1186/s12938-023-01075-1
Zuhdi, A. M., Lutfina, E., & Saraswati, G. W. (2026). Developing an Integrated Capital Assistance and Community Training System Using Agile Scrum. 10(2), 1081–1093.
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2026 Feri Iskandar, Ibnu Anugrah, Deosa Putra Caniago, Yopy Mardiansyah, Galang Mario Alpindra

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.






















Moraref
PKP Index
Indonesia OneSearch
OCLC Worldcat
Index Copernicus
Scilit
