PhDThis thesis describes research in the computational analysis of harmony in
western art music, focussing particularly on improving the accuracy and
information-richness of key and chord extraction from digital score data.
It is argued that a greater sophistication in automatic harmony analysis
is an important contribution to the field of computational musicology.
Initial experiments use hidden Markov models to predict key and modulation
from automatically labelled chord sequences. Model parameters
are based on heuristically formulated chord and key weightings derived
from Sch¨onberg’s harmonic theory and the key and chord ratings resulting
from perceptual experiments with listeners. The music theory models
are shown to outperform the perceptual models both in terms of key accuracy
and modelling the precise moment of key change. All of the models
perform well enough to generate descriptive data about modulatory frequency,
modulatory type and key distance.
A robust method of classifying underlying chord types from elaborated
keyboard music is then detailed. The method successfully distinguishes
between essential and inessential notes, for example, passing notes and
neighbour notes, and combines note classification information with tertian
chord potential to measure the harmonic importance of a note. Existing
approaches to automatic chord classification are unsuitable for use with
complex textures and are restricted to triads and simple sevenths. An
important goal is therefore to recognise a much broader set of chords, including
complex chord types such as 9ths, 11ths and 13ths. This level
of detail is necessary if the methods are to supply sophisticated information
about the harmonic techniques of composers. Testing on the first
twenty-four preludes of J. S. Bach’s Well Tempered Clavier, hand annotated
by the author, a state of the art approach achieves 22.1% accuracy;
our method achieves 55% accuracy.Engineering and
Physical Sciences Research Council (EPSRC) DTA studentship
