Physical Models for Fast Estimation of Guitar String, Fret and Plucking Position

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Abstract

In this paper, a novel method for analyzing guitar performances is proposed. It is both fast and effective at extracting the activated string, fret, and plucking position from guitar recordings. The method is derived from guitar-string physics and, unlike the state of the art, does not require audio recordings as training data. A maximum a posteriori classifier is proposed for estimating the string and fret based on a simulated model of feature vectors while the plucking position is estimated using estimated inharmonic partials. The method extracts features from audio with a pitch estimator that estimates also the inharmonicity of the string. The string and fret classifier is evaluated on recordings of an electric and acoustic guitar under noisy conditions. The performance is comparable to the state of the art, and the performance is shown to degrade at SNRs below 20 dB. The plucking position estimator is evaluated in a proof-of-concept experiment with sudden changes of string, fret and plucking positions, which shows that these can be estimated accurately. The proposed method operates on individual 40 ms segments and is thus suitable for high-tempo and real-time applications.
Original languageEnglish
Title of host publication2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, WASPAA 2019
Number of pages5
Place of PublicationNew Paltz, NY
PublisherIEEE
Publication dateOct 2019
Pages155-159
Article number8937157
ISBN (Electronic)9781728111230
DOIs
Publication statusPublished - Oct 2019
EventIEEE Workshop on Applications of Signal Processing to Audio and Acoustics 2019 - New Paltz, United States
Duration: 20 Oct 201923 Jan 2020

Conference

ConferenceIEEE Workshop on Applications of Signal Processing to Audio and Acoustics 2019
Country/TerritoryUnited States
CityNew Paltz
Period20/10/201923/01/2020
SeriesIEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)
ISSN1947-1629

Keywords

  • physical modelling
  • statistical signal processing
  • parametric pitch estimation
  • Music information retrieval

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