Estimation of Guitar String, Fret and Plucking Position Using Parametric Pitch Estimation

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Resumé

In this paper a fast yet effective method is proposed for analyzing guitar performances. Specifically, the activated string and fret as well as the location of the plucking event along the guitar string are extracted from guitar signal recordings. The method is based on a parametric pitch estimator and is derived from a physically meaningful model that includes inharmonicity. A maximum a posteriori classifier is proposed, which requires training data captured from only one fret per string. The classifier is tested on recordings of electric and acoustic guitar and performs well: the average absolute error of string and fret classification is 1.5%, while the error rate varies depending on the fret used for training. The plucking position estimator is the minimizer of the log spectral distance between the amplitudes of the observed signal and the plucking model and it is evaluated in proof-of-concept experiments with sudden changes of string, fret and plucking positions, which can be estimated accurately. Unlike the state of the art, the proposed method works on very short segments, which makes it suitable for high-tempo and real-time applications.
OriginalsprogEngelsk
TitelIEEE International Conference on Acoustics, Speech and Signal Processing
Publikationsdato2019
StatusAccepteret/In press - 2019

Fingeraftryk

Classifiers
Acoustics
Experiments

Citer dette

Hjerrild, J. M., & Christensen, M. G. (Accepteret/In press). Estimation of Guitar String, Fret and Plucking Position Using Parametric Pitch Estimation. I IEEE International Conference on Acoustics, Speech and Signal Processing
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title = "Estimation of Guitar String, Fret and Plucking Position Using Parametric Pitch Estimation",
abstract = "In this paper a fast yet effective method is proposed for analyzing guitar performances. Specifically, the activated string and fret as well as the location of the plucking event along the guitar string are extracted from guitar signal recordings. The method is based on a parametric pitch estimator and is derived from a physically meaningful model that includes inharmonicity. A maximum a posteriori classifier is proposed, which requires training data captured from only one fret per string. The classifier is tested on recordings of electric and acoustic guitar and performs well: the average absolute error of string and fret classification is 1.5{\%}, while the error rate varies depending on the fret used for training. The plucking position estimator is the minimizer of the log spectral distance between the amplitudes of the observed signal and the plucking model and it is evaluated in proof-of-concept experiments with sudden changes of string, fret and plucking positions, which can be estimated accurately. Unlike the state of the art, the proposed method works on very short segments, which makes it suitable for high-tempo and real-time applications.",
author = "Hjerrild, {Jacob M{\o}ller} and Christensen, {Mads Gr{\ae}sb{\o}ll}",
year = "2019",
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Hjerrild, JM & Christensen, MG 2019, Estimation of Guitar String, Fret and Plucking Position Using Parametric Pitch Estimation. i IEEE International Conference on Acoustics, Speech and Signal Processing.

Estimation of Guitar String, Fret and Plucking Position Using Parametric Pitch Estimation. / Hjerrild, Jacob Møller; Christensen, Mads Græsbøll.

IEEE International Conference on Acoustics, Speech and Signal Processing. 2019.

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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N2 - In this paper a fast yet effective method is proposed for analyzing guitar performances. Specifically, the activated string and fret as well as the location of the plucking event along the guitar string are extracted from guitar signal recordings. The method is based on a parametric pitch estimator and is derived from a physically meaningful model that includes inharmonicity. A maximum a posteriori classifier is proposed, which requires training data captured from only one fret per string. The classifier is tested on recordings of electric and acoustic guitar and performs well: the average absolute error of string and fret classification is 1.5%, while the error rate varies depending on the fret used for training. The plucking position estimator is the minimizer of the log spectral distance between the amplitudes of the observed signal and the plucking model and it is evaluated in proof-of-concept experiments with sudden changes of string, fret and plucking positions, which can be estimated accurately. Unlike the state of the art, the proposed method works on very short segments, which makes it suitable for high-tempo and real-time applications.

AB - In this paper a fast yet effective method is proposed for analyzing guitar performances. Specifically, the activated string and fret as well as the location of the plucking event along the guitar string are extracted from guitar signal recordings. The method is based on a parametric pitch estimator and is derived from a physically meaningful model that includes inharmonicity. A maximum a posteriori classifier is proposed, which requires training data captured from only one fret per string. The classifier is tested on recordings of electric and acoustic guitar and performs well: the average absolute error of string and fret classification is 1.5%, while the error rate varies depending on the fret used for training. The plucking position estimator is the minimizer of the log spectral distance between the amplitudes of the observed signal and the plucking model and it is evaluated in proof-of-concept experiments with sudden changes of string, fret and plucking positions, which can be estimated accurately. Unlike the state of the art, the proposed method works on very short segments, which makes it suitable for high-tempo and real-time applications.

M3 - Article in proceeding

BT - IEEE International Conference on Acoustics, Speech and Signal Processing

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Hjerrild JM, Christensen MG. Estimation of Guitar String, Fret and Plucking Position Using Parametric Pitch Estimation. I IEEE International Conference on Acoustics, Speech and Signal Processing. 2019