Pickup position and plucking point estimation on an electric guitar via autocorrelation

Abstract

This paper proposes a technique that estimates the locations along the string of the plucking event and the magnetic pickup of an electric guitar based on the autocorrelation of the spectral peaks. To improve accuracy, a method is introduced to flatten the spectrum before applying the autocorrelation function to the spectral peaks. The minimum mean squared error between the autocorrelation of the observed data and the electric guitar model is found in order to estimate the model parameters. The accuracy of the algorithm is tested on various plucking positions on all open strings for each pickup configuration. The accuracy of the proposed method for various plucking dynamics and fret positions is also evaluated. The method yields accurate results: the average absolute errors of the pickup position and plucking point estimates for single pickups are 3.53 and 5.11 mm, respectively, and for mixed pickups are 8.47 and 9.95 mm, respectively. The model can reliably distinguish which pickup configuration is selected using the pickup position estimates. Moreover, the method is robust to changes in plucking dynamics and fret positions.

Guitar Effects Recognition and Parameter Estimation With Convolutional Neural Networks

“…Most works are based on features extracted from recorded sounds [9,[12][13][14][15] but there are also examples of estimation based on guitar-string physical models [16,17] for high-tempo and real-time applications. Plucking and pickup position estimation has also been studied, with Mohamad et al exploring solutions based on spectral features (comparing recordings with string models) and autocorrelation [18]. The study was also extended to the case of nonlinear audio effects in the signal chain [19].…”