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 language | English |
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Title of host publication | ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Number of pages | 5 |
Publisher | IEEE |
Publication date | May 2019 |
Pages | 151-155 |
ISBN (Print) | 978-1-4799-8132-8 |
ISBN (Electronic) | 978-1-4799-8131-1 |
DOIs | |
Publication status | Published - May 2019 |
Event | 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Brighton, United Kingdom Duration: 12 May 2019 → 17 May 2019 https://2019.ieeeicassp.org/ |
Conference
Conference | 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
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Country/Territory | United Kingdom |
City | Brighton |
Period | 12/05/2019 → 17/05/2019 |
Internet address |
Series | I E E E International Conference on Acoustics, Speech and Signal Processing. Proceedings |
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ISSN | 1520-6149 |
Keywords
- Machine Learning
- Music Information Retrieval
- Parametric Pitch Estimation
- Physical Modeling
- Statistical Signal Processing