Classification of plucking style and plucking expression of an acoustic guitar notes based on impulse response

Minakshi Pradeep Atre

doi:10.1117/12.2675654

10 April 2023 Classification of plucking style and plucking expression of an acoustic guitar notes based on impulse response

Minakshi Pradeep Atre

Author Affiliations +

Proceedings Volume 12616, International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2022); 126160R (2023) https://doi.org/10.1117/12.2675654
Event: International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2022), 2022, Istanbul, Turkey

Abstract

The work is focused on the acoustic guitar music note generation and validation. The music notes are recorded in the acoustic studio and the two playing styles of acoustic guitar are considered for the study. The acoustic guitar note is generated by directly using the finger or by using a small triangular shaped plectrum, known as pick. The music notes are collected on the basis of these two playing styles, finger plucked notes and plectrum plucked notes. They are termed as plucked and picked guitar notes respectively. The music notes are analyzed using digital processing technique named as cepstral domain window method. The analysis helps to separate the excitation signal of that note and the impulse response of the acoustic guitar note. The impulse response of a music note is derived and then used to generate the other music notes. The generated music notes are further classified by using machine learning algorithms. This classification of generated notes is the part of the validation of the research work. The classification of recorded music notes gives 100 % result but the generated music notes from an impulse response provides strong base for the mathematical model of the acoustic guitar note. The results are discussed in this paper.

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Minakshi Pradeep Atre "Classification of plucking style and plucking expression of an acoustic guitar notes based on impulse response", Proc. SPIE 12616, International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2022), 126160R (10 April 2023); https://doi.org/10.1117/12.2675654

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KEYWORDS

Acoustics

Machine learning

Mathematical modeling

Frequency response

Convolution

Support vector machines

Windows

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