Estimating Performance Parameters from Electric Guitar Recordings
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The main motivation of this thesis is to explore several techniques for estimating electric guitar synthesis parameters to replicate the sound of popular guitarists. Many famous guitar players are recognisable by their distinctive electric guitar tone, and guitar enthusiasts would like to play or obtain their favourite guitarist’s sound on their own guitars. This thesis starts by exploring the possibilities of replicating a target guitar sound, given an input guitar signal, using a digital filter. A preliminary step is taken where a technique is proposed to transform the sound of a pickup into another on the same electric guitar. A least squares estimator is used to obtain the coefficients of a finite impulse response (FIR) filter to transform the sound. The technique yields good results which are supported by a listening test and a spectral distance measure showing that up to 99% of the difference between input and target signals is reduced. The robustness of the filters towards changes in repetitions, plucking positions, dynamics and fret positions are also discussed. A small increase in error was observed for different repetitions; moderate errors arose when the plucking position and dynamic were varied; and there were large errors when the training and test data comprised different notes (fret positions). Secondly, this thesis explored another possible way to replicate the sound of popular guitarists in order to overcome the limitations provided by the first approach. Instead of directly morphing one sound into another, replicating the sound with electric guitar synthesis provides flexibility that requires some parameters. Three approaches to estimate the pickup and plucking positions of an electric guitar are discussed in this thesis which are the Spectral Peaks (SP), Autocorrelation of Spectral Peaks (AC-SP) and Log-correlation of Spectral Peaks (LC-SP) methods. LC-SP produces the best results with faster computation, where the median absolute errors for pickup and plucking position estimates are 1.97 mm and 2.73 mm respectively using single pickup data and the errors increased slightly for mixed pickup data. LC-SP is also shown to be robust towards changes in plucking dynamics and fret positions, where the median absolute errors for pickup and plucking position estimates are less than 4 mm. The Polynomial Regression Spectral Flattening (PRSF) method is introduced to compensate the effects of guitar effects, amplifiers, loudspeakers and microphones. The accuracy of the estimates is then tested on several guitar signal chains, where the median absolute errors for pickup and plucking position estimates range from 2.04 mm to 7.83 mm and 2.98 mm to 27.81 mm respectively.
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