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

Jacob Møller Hjerrild, Mads Græsbøll Christensen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2 Citations (Scopus)
313 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages5
PublisherIEEE
Publication dateMay 2019
Pages151-155
ISBN (Print)978-1-4799-8132-8
ISBN (Electronic)978-1-4799-8131-1
DOIs
Publication statusPublished - May 2019
Event2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Brighton, United Kingdom
Duration: 12 May 201917 May 2019
https://2019.ieeeicassp.org/

Conference

Conference2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Country/TerritoryUnited Kingdom
CityBrighton
Period12/05/201917/05/2019
Internet address
SeriesI E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings
ISSN1520-6149

Keywords

  • Machine Learning
  • Music Information Retrieval
  • Parametric Pitch Estimation
  • Physical Modeling
  • Statistical Signal Processing

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