Pickup position and plucking point estimation on an electric guitar

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.

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

“…In this section, we compare the absolute errors for the current method (ASP2) with our previous method (MFS). 18 Our previous method also uses a frequency domain approach where a period of the tone is selected and its Fourier series is calculated. Then, we calculate the electric guitar models for single pickup in Eq.…”

“…This paper extends recent research on estimating the pickup position and plucking point of electric guitar tones. 18 The parameters are estimated using a frequency-domain approach, where the parameters of the electric guitar model that best fit the observed data are chosen. In this paper, we propose an improved method to estimate the locations of the pickup and plucking events based on the autocorrelation of the spectral peaks.…”

Guitar Effects Recognition and Parameter Estimation With Convolutional Neural Networks

“…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].…”

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

“…Both methods [20,21] operate on multiple segments, each in the order of 100 ms, which makes them unsuitable for hightempo and real-time applications. Papers with studies on estimation of the plucking location on the guitar string have used frequencydomain [22][23][24] and time-domain [25] approaches, but only for open strings or by assuming a known pitch and string-fret position or the estimates were obtained with an under-saddle pickup.…”