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.
Authors
Mohamad, ZulfadhliCollections
- Theses [4116]