Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.765985
Title: Towards the automatic analysis of metric modulations
Author: Quinton, Elio
ISNI:       0000 0004 7652 9752
Awarding Body: Queen Mary University of London
Current Institution: Queen Mary, University of London
Date of Award: 2017
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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.
Supervisor: Not available Sponsor: EPSRC ; Omnifone Ltd
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.765985  DOI: Not available
Keywords: C4DM ; Electronic Engineering and Computer Science ; Center for Digital Music ; Music Information Retrieval ; metric modulations
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