Timbre transfer of classical guitars using impulse response and a laser displacement sensor

Che-Hsu Yeh; Chii-Chang Chen

doi:10.59400/sv4106

Timbre transfer of classical guitars using impulse response and a laser displacement sensor

Che-Hsu Yeh
Department of Optics and Photonics, National Central University, Taoyuan City 320317, Taiwan
Chii-Chang Chen
Department of Optics and Photonics, National Central University, Taoyuan City 320317, Taiwan

Article ID: 4106

DOI: https://doi.org/10.59400/sv4106

Keywords: Classical guitars, timbre, impulse response, laser displacement sensor

Abstract

Conventional characterization of musical instruments relies on microphones, whose measurements depend strongly on distance, sound radiation directionality, and the non-uniform frequency responses of microphones and preamplifiers, often introducing distortions. Laser displacement sensors, widely used for vibration and surface measurements, offer a higher maximum displacement–to–resolution ratio than laser Doppler vibrometry. Measuring instrument vibrations with such sensors enables the capture of a more intrinsic acoustic source. This study demonstrates timbre transfer from a hand-made guitar to a José Ramírez guitar using convolution with impulse responses obtained from both a laser displacement sensor and a microphone. Timbre similarity is evaluated via cross-correlation of acoustic spectra between original and transferred notes. For 33 notes (E2–C5), the laser-based method yields higher cross-correlation in 28 cases compared to the microphone-based approach. The results highlight the distinction between vibration-based and pressure-based timbre characterization. Microphone measurements capture far-field sound pressure along with environmental noise, whereas laser displacement sensing directly reflects the instrument’s vibrational behavior. Consequently, laser-based impulse responses provide a more intrinsic representation of acoustic spectra and enable more accurate timbre transfer. After recording a performance, the timbre of one instrument can therefore be transferred more precisely to another using impulse responses derived from laser displacement sensors.

Published

2026-05-14

How to Cite

Yeh, C.-H., & Chen, C.-C. (2026). Timbre transfer of classical guitars using impulse response and a laser displacement sensor. Sound & Vibration, 6(3). https://doi.org/10.59400/sv4106

Download Citation

Issue

Vol. 60 No. 3 (2026): In Progress

Section

Article

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

References

[1]Rossing TD. Springer Handbook of Acoustics, Springer Handbooks. Springer; 2014. doi: 10.1007/978-1-4939-0755-7

[2]Penttinen H, Välimäki V. A time-domain approach to estimating the plucking point of guitar tones obtained with an under-saddle pickup. Applied Acoustics. 2004; 65(12): 1207–1220. doi: 10.1016/j.apacoust.2004.04.008

[3]Caroline T. An Interdisciplinary Study of the Timbre of the Classical Guitar [PhD Thesis]. McGill University; 2004. Available online: https://escholarship.mcgill.ca/concern/theses/k643b175t

[4]Han J. Improved Skyline-BP Network for Multi-Track MIDI Music Melody Extraction and Style Classification. HighTech and Innovation Journal. 2025; 6(4): 1170–1184. doi: 10.28991/HIJ-2025-06-04-04

[5]Esquef PAA, Välimäki V, Karjalainen M. Restoration and enhancement of solo guitar recordings based on sound source modeling. Journal of the Audio Engineering Society. 2002; 50(4): 227–236. Available online: https://www.researchgate.net/publication/234138310_Restoration_and_Enhancement_of_Solo_

[6]Laurson M, Erkut C, Välimäki V, et al. Methods for Modeling Realistic Playing in Acoustic Guitar Synthesis. Computer Music Journal. 2001; 25(3): 38–49. doi: 10.1162/014892601753189529

[7]Ekmen Ş, Karadoğan C, Şeker ŞS. Investigation of Timbral Qualities of Guitar Using Wavelet Analysis. Traitement du Signal. 2021; 38(2): 401–411. doi: 10.18280/ts.380218

[8]An L, Qiu Y, Wang X, et al. Low resolution fourier synthesis modelling for underwater acoustic channel impulse response. Applied Acoustics. 2022; 188: 108596. doi: 10.1016/j.apacoust.2021.108596

[9]Huang G, Benesty J, Chen J. Dimensionality Reduction of Room Acoustic Impulse Responses and Applications to System Identification. IEEE Signal Processing Letters. 2023; 30: 1107–1111. doi: 10.1109/LSP.2023.3306619

[10]Dogariu LM, Benesty J, Paleologu C, et al. Identification of Room Acoustic Impulse Responses via Kronecker Product Decompositions. IEEE/ACM Transactions on Audio, Speech, and Language Processing. 2022; 30: 2828–2841. doi: 10.1109/TASLP.2022.3202128

[11]Tıraş M. Variation of the room modes and impulse response according to surface absorption properties in a non-rectangular room. MEGARON / Yıldız Technical University, Faculty of Architecture E-Journal. 2025; 93–104. doi: 10.14744/megaron.2025.94580

[12]Du Y, Gan C, Luo A, et al. Learning Neural Acoustic Fields. In: Proceedings of the Advances in Neural Information Processing Systems 35; 28 November–9 December 2022; New Orleans, LA, USA. pp. 3165–3177. doi: 10.52202/068431-0229

[13]Xydis A, Perraudin N, Rust R, et al. GIR dataset: A geometry and real impulse response dataset for machine learning research in acoustics. Applied Acoustics. 2023; 208: 109333. doi: 10.1016/j.apacoust.2023.109333

[14]Lan Z, Zhao M, Zheng C, et al. Acoustic Volume Rendering for Neural Impulse Response Fields. In: Proceedings of the Advances in Neural Information Processing Systems 37; 10–15 December 2024; Vancouver, BC, Canada. pp. 44600–44623. doi: 10.52202/079017-1417

[15]Hidaka T, Nishihara N. Favorable reverberation time in concert halls revisited for piano and violin solos. The Journal of the Acoustical Society of America. 2022; 151(3): 2192–2206. doi: 10.1121/10.0009931

[16]Matsutani A. Measurement of frequency characteristics under various tuning conditions in violin performances. Acoustical Science and Technology. 2023; 44(3): 302–308. doi: 10.1250/ast.44.302

[17]Lloyd T, Gaydecki P, Ginsborg J, et al. Violin mpulse esponse ength and erceptions of cceptability. Journal of New Music Research. 2018; 47(3): 264–269. doi: 10.1080/09298215.2017.1421668

[18]Elie B, Gautier F, David B. Acoustic signature of violins based on bridge transfer mobility measurements. The Journal of the Acoustical Society of America. 2014; 136(3): 1385–1393. doi: 10.1121/1.4892762

[19]Pradeep Atre M, Apte S. Impulse Response Modeling of the Box Shaped Acoustic Guitar. In: Music in Health and Diseases. IntechOpen; 2022. doi: 10.5772/intechopen.99757

[20]Tronchin L, Tarabusi V. Acoustical analysis in Ancient Violins. In: Proceedings of the International Symposium on Musical Acoustics (ISMA2004); 31 March–3 April 2004; Nara, Japan. doi: 10.13140/2.1.1711.7442

[21]Sleator DD. Simulating musical instruments using their impulse responses. The Journal of the Acoustical Society of America. 1985; 77(S1): S90–S91. doi: 10.1121/1.2022586

[22]Nagata M, Saito A. Sound synthesis of drums based on modal representation of transfer functions. Journal of Vibration and Control. 2025; 31(15–16): 3438–3449. doi: 10.1177/10775463241272937

[23]Karjalainen M, Välimäki V, Henri P, et al. DSP equalization of electret film pickup for the acoustic guitar. Journal of the Audio Engineering Society. 2000; 48(12): 1183–1193.

[24]Boullosa RR. Vibration measurements in the classical guitar. Applied Acoustics. 2002; 63(3): 311–322. doi: 10.1016/S0003-682X(01)00037-8

[25]Mihălcică M, Stanciu MD, Vlase S. Frequency Response Evaluation of Guitar Bodies with Different Bracing Systems. Symmetry. 2020; 12(5): 795. doi: 10.3390/sym12050795

[26]Rezaei F. Laser Doppler vibrometer and accelerometer for vibrational analysis of the automotive components during Simulink simulation for validation. arXiv preprint. 2023; doi: 10.48550/arXiv.2304.11054

[27]Rothberg SJ, Allen MS, Castellini P, et al. An international review of laser Doppler vibrometry: Making light work of vibration measurement. Optics and Lasers in Engineering. 2017; 99: 11–22. doi: 10.1016/j.optlaseng.2016.10.023

[28]Zoran A, Welch S, Hunt WD. A platform for manipulation and examination of the acoustic guitar: The Chameleon Guitar. Applied Acoustics. 2012; 73(4): 338–347. doi: 10.1016/j.apacoust.2011.10.004

[29]Rau M, Abel JS, James D, et al. Electric-to-acoustic pickup processing for string instruments: An experimental study of the guitar with a hexaphonic pickup. The Journal of the Acoustical Society of America. 2021; 150(1): 385–397. doi: 10.1121/10.0005540

[30]Bissinger G, Oliver D. 3-D laser vibrometry on legendary old Italian violins. Sound and Vibration. 2007; 41: 10–15. Available online: http://www.sandv.com/downloads/0707biss.pdf

[31]Skrodzka E, Łapa A, Linde BBJ, et al. Modal parameters of two incomplete and complete guitars differing in the bracing pattern of the soundboard. The Journal of the Acoustical Society of America. 2011; 130(4): 2186–2194. doi: 10.1121/1.3626194

[32]Su KC, Hsieh TY, Lin WC, et al. A Three-Dimensional Method for Analysis of the Body Mode of Classical Guitars Using a Laser Displacement Sensor. Sensors. 2024; 24(16): 5147. doi: 10.3390/s24165147

[33]Havelock D, Kuwano S, Vorländer M. Handbook of Signal Processing in Acoustics. Springer; 2008. doi: 10.1007/978-0-387-30441-0

[34]Romanillos JL. Antonio de Torres, Guitar Maker—His Life and Work. Bold Strummer Ltd; 1997.

[35]Lee MK, Hosseini Fouladi M, Namasivayam SN. Mathematical Modelling and Acoustical Analysis of Classical Guitars and Their Soundboards. Advances in Acoustics and Vibration. 2016; 2016: 1–10. doi: 10.1155/2016/6084230

[36]Fahy F, Gardonio P. Sound and Structural Vibration. Elsevier; 2007. doi: 10.1016/B978-0-12-373633-8.X5000-5

[37]Lu S, Guo C. Improving Sensing Measurements Using Laser Self-Mixing Interference in Non-Line-of-Sight Optical Communication via Systems. HighTech and Innovation Journal. 2024; 5(4): 1038–1054. doi: 10.28991/HIJ-2024-05-04-012

[38]Williams EG. Fourier Acoustics. Elsevier; 1999. doi: 10.1016/B978-0-12-753960-7.X5000-1

This paper introduces a universal framework for understanding the vibration responses of systems subjected to harmonic excitation. By examining a simplified cylinder-spring-damper model, the study refurbishes traditional scaling methods for the excitation frequency ratio and critical damping ratio. The findings indicate that in damped systems, the maximum amplitude of vibration does not align with the natural frequency. This observation leads to the introduction of a new scaling method for reduced frequency. This new approach aligns resonance peaks at the new reduced velocity of 1.0 across different damping ratios, providing a consistent characterization of vibration behavior. A new critical damping ratio of 0.707 is identified for an excited system as opposed to the traditional damping ratio of 1.0 for an unexcited system. Key properties such as maximum amplitude, phase lag, bandwidth, and quality factor are analyzed, demonstrating that the proposed reduced frequency and critical damping ratio effectively capture the dynamics of both damped and undamped excited systems. The findings offer significant insights for practical applications in engineering and various scientific fields.

This study makes significant contributions to the field of ultrasonic testing (UT) by offering a novel approach to the identification of artificially introduced defects within Japanese cedar wood (Cryptomeria japonica). The findings are of particular relevance for the heritage conservation and construction sectors, where non-invasive defect detection is paramount. The study establishes a robust framework for assessing the structural integrity of timber by correlating ultrasonic wave velocity reductions with defect size and distribution. Big-sized defects led to more substantial decreases in wave velocity. The study establishes a robust framework for assessing the structural integrity of historical timber by correlating ultrasonic wave velocity reductions with defect size and distribution. This framework has the potential to be applicable to diverse wood species and defect types.

The control of vehicle interior noise has become a critical metric for assessing noise, vibration, and harshness (NVH) in vehicles. During the initial phases of vehicle development, accurately predicting the impact of road noise on interior noise is essential for reducing noise levels and expediting the product development cycle. In recent years, data-driven methods based on machine learning have gained significant attention due to their robust capability in navigating complex data mapping relationships. Notably, surrogate models have demonstrated exceptional performance in this domain. Numerous researchers have integrated diverse intelligent algorithms into the study of vehicle noise, leveraging advantages such as the elimination of precise modeling requirements, extensive solution space exploration, continuous learning from data, and robust algorithmic versatility. However, in NVH engineering applications, data-driven models face inherent limitations, particularly in interpretability and stability. To address these issues, this paper introduces an improved Long Short-Term Memory (LSTM) network that combines knowledge and data. Inspired by the physical information neural network concept, this approach incorporates values calculated through empirical formulas into the neural network as constraints. Comparative assessments with traditional LSTM networks highlight the advantages of this deep learning model. By integrating empirical formulas constraints, the model not only enhances interpretability but also achieves robust generalization with fewer data samples. The proposed method is validated on a specific vehicle model, showing significant improvements in prediction accuracy and efficiency.