dc.contributor.author | Tian, Mi | |
dc.date.accessioned | 2017-07-07T13:30:05Z | |
dc.date.available | 2017-07-07T13:30:05Z | |
dc.date.issued | 2017-10-01 | |
dc.date.submitted | 2017-07-07T13:45:02.047Z | |
dc.identifier.citation | Tian, M. 2017. A Cross-Cultural Analysis of Music Structure. Queen Mary University of London | en_US |
dc.identifier.uri | http://qmro.qmul.ac.uk/xmlui/handle/123456789/24782 | |
dc.description | PhD | en_US |
dc.description.abstract | Music signal analysis is a research field concerning the extraction of meaningful information
from musical audio signals. This thesis analyses the music signals from the note-level
to the song-level in a bottom-up manner and situates the research in two Music information
retrieval (MIR) problems: audio onset detection (AOD) and music structural
segmentation (MSS).
Most MIR tools are developed for and evaluated on Western music with specific musical
knowledge encoded. This thesis approaches the investigated tasks from a cross-cultural
perspective by developing audio features and algorithms applicable for both Western and
non-Western genres. Two Chinese Jingju databases are collected to facilitate respectively
the AOD and MSS tasks investigated.
New features and algorithms for AOD are presented relying on fusion techniques. We
show that fusion can significantly improve the performance of the constituent baseline
AOD algorithms. A large-scale parameter analysis is carried out to identify the relations
between system configurations and the musical properties of different music types.
Novel audio features are developed to summarise music timbre, harmony and rhythm for
its structural description. The new features serve as effective alternatives to commonly
used ones, showing comparable performance on existing datasets, and surpass them on
the Jingju dataset. A new segmentation algorithm is presented which effectively captures
the structural characteristics of Jingju. By evaluating the presented audio features and
different segmentation algorithms incorporating different structural principles for the
investigated music types, this thesis also identifies the underlying relations between audio
features, segmentation methods and music genres in the scenario of music structural
analysis. | en_US |
dc.description.sponsorship | China Scholarship Council
EPSRC C4DM Travel Funding,
EPSRC Fusing Semantic and Audio Technologies for Intelligent Music Production and
Consumption (EP/L019981/1),
EPSRC Platform Grant on Digital Music (EP/K009559/1),
European Research Council project CompMusic, International Society for Music Information Retrieval Student Grant,
QMUL Postgraduate Research Fund,
QMUL-BUPT Joint Programme Funding
Women in Music Information Retrieval Grant. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Queen Mary University of London | en_US |
dc.rights | The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author | |
dc.subject | Electronic engineering and computer science | en_US |
dc.subject | Music signal analysis | en_US |
dc.subject | audio onset detection | en_US |
dc.subject | music structural segmentation | en_US |
dc.subject | C4DM | en_US |
dc.title | A Cross-Cultural Analysis of Music Structure | en_US |
dc.type | Thesis | en_US |