PhDThe comparison of world music cultures has been considered in musicological
research since the end of the 19th century. Traditional methods from the
field of comparative musicology typically involve the process of manual music
annotation. While this provides expert knowledge, the manual input is timeconsuming
and limits the potential for large-scale research. This thesis considers
computational methods for the analysis and comparison of world music cultures.
In particular, Music Information Retrieval (MIR) tools are developed for processing
sound recordings, and data mining methods are considered to study
similarity relationships in world music corpora.
MIR tools have been widely used for the study of (mainly) Western music.
The first part of this thesis focuses on assessing the suitability of audio descriptors
for the study of similarity in world music corpora. An evaluation strategy
is designed to capture challenges in the automatic processing of world music
recordings and different state-of-the-art descriptors are assessed.
Following this evaluation, three approaches to audio feature extraction are
considered, each addressing a different research question. First, a study of
singing style similarity is presented. Singing is one of the most common forms
of musical expression and it has played an important role in the oral transmission
of world music. Hand-designed pitch descriptors are used to model aspects of the
singing voice and clustering methods reveal singing style similarities in world
music. Second, a study on music dissimilarity is performed. While musical
exchange is evident in the history of world music it might be possible that some
music cultures have resisted external musical influence. Low-level audio features
are combined with machine learning methods to find music examples that stand
out in a world music corpus, and geographical patterns are examined. The
last study models music similarity using descriptors learned automatically with
deep neural networks. It focuses on identifying music examples that appear to
be similar in their audio content but share no (obvious) geographical or cultural
links in their metadata. Unexpected similarities modelled in this way uncover
possible hidden links between world music cultures.
This research investigates whether automatic computational analysis can
uncover meaningful similarities between recordings of world music. Applications
derive musicological insights from one of the largest world music corpora
studied so far. Computational analysis as proposed in this thesis advances the
state-of-the-art in the study of world music and expands the knowledge and
understanding of musical exchange in the world.Queen Mary Principal’s research studentship
