dc.contributor.author | Quinton, Elio | |
dc.date.accessioned | 2017-09-27T13:25:06Z | |
dc.date.available | 2017-09-27T13:25:06Z | |
dc.date.issued | 2017-08-03 | |
dc.date.submitted | 2017-09-27T13:48:37.413Z | |
dc.identifier.citation | Quinton, E. 2017. Towards the Automatic Analysis of Metric Modulations. Queen Mary University of London | en_US |
dc.identifier.uri | http://qmro.qmul.ac.uk/xmlui/handle/123456789/25936 | |
dc.description | PhD | en_US |
dc.description.abstract | The metrical structure is a fundamental aspect of music, yet its automatic analysis
from audio recordings remains one of the great challenges of Music Information Retrieval
(MIR) research. This thesis is concerned with addressing the automatic analysis
of changes of metrical structure over time, i.e. metric modulations. The evaluation of
automatic musical analysis methods is a critical element of the MIR research and is
typically performed by comparing the machine-generated estimates with human expert
annotations, which are used as a proxy for ground truth. We present here two new
datasets of annotations for the evaluation of metrical structure and metric modulation
estimation systems. Multiple annotations allowed for the assessment of inter-annotator
(dis)agreement, thereby allowing for an evaluation of the reference annotations used to
evaluate the automatic systems. The rhythmogram has been identified in previous research
as a feature capable of capturing characteristics of rhythmic content of a music
recording. We present here a direct evaluation of its ability to characterise the metrical
structure and as a result we propose a method to explicitly extract metrical structure
descriptors from it. Despite generally good and increasing performance, such rhythm
features extraction systems occasionally fail. When unpredictable, the failures are a
barrier to usability and development of trust in MIR systems. In a bid to address this
issue, we then propose a method to estimate the reliability of rhythm features extraction.
Finally, we propose a two-fold method to automatically analyse metric modulations from
audio recordings. On the one hand, we propose a method to detect metrical structure
changes from the rhythmogram feature in an unsupervised fashion. On the other hand,
we propose a metric modulations taxonomy rooted in music theory that relies on metrical
structure descriptors that can be automatically estimated. Bringing these elements
together lays the ground for the automatic production of a musicological interpretation
of metric modulations. | en_US |
dc.description.sponsorship | EPSRC award 1325200 and Omnifone Ltd. | 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 | C4DM | en_US |
dc.subject | Electronic Engineering and Computer Science | en_US |
dc.subject | Center for Digital Music | en_US |
dc.subject | Music Information Retrieval | en_US |
dc.subject | metric modulations | en_US |
dc.title | Towards the Automatic Analysis of Metric Modulations | en_US |
dc.type | Thesis | en_US |