|dc.identifier.citation||Kosta, K. 2017. Computational Modelling and Quantitative Analysis of Dynamics in Performed Music. Queen Mary University of London||en_US
|dc.description.abstract||Musical dynamics- loudness and changes in loudness - forms one of the key aspects of expressive music performance. Surprisingly this rather important research area has received
little attention. A reason is the fact that while the concept of dynamics is related to signal
amplitude, which is a low-level feature, the process of deriving perceived loudness from the
signal is far from straightforward.
This thesis advances the state of the art in the analysis of perceived loudness by modelling
dynamic variations in expressive music performance and by studying the relation between
dynamics in piano recordings and markings in the score. In particular, we show that dynamic
changes: a) depend on the evolution of the performance and the local context of the piece;
b) correspond to important score markings and music structures; and, c) can reflect wide
divergences in performers' expressive strategies within and across pieces.
In a preparatory stage, dynamic changes are obtained by linking existing music audio
and score databases. All studies in this thesis are based on loudness levels extracted from
2000 recordings of 44 Mazurkas by Frederic Chopin. We propose a new method for efficiently
aligning and annotating the data in score beat time representation, based on dynamic time
warping applied to chroma features. Using the score-aligned recordings, we examine the
relationship between loudness values and dynamic level categories.
The research can be broadly categorised into two parts. The first investigates how dynamic
markings map to performed loudness levels. Empirical results show that different dynamic
markings do not correspond to fixed loudness thresholds. Rather, the important factors are the
relative loudness of neighbouring markings, the inter-relations of nearby markings and other
score information, the structural location of the markings, and the creative license exercised
by the performer in inserting further interpretive dynamic shaping.
The second part seeks to determine how changes in loudness levels map to score features
using statistical change-point techniques. The results show that significant dynamic score
markings do indeed correspond to change points. Furthermore, evidence suggests that change
points in score positions without dynamic markings highlight structurally salient events or
events based on temporal changes.
In a separate bidirectional study, we investigate the relationship between dynamic mark-
ings in the score and performed loudness using machine learning techniques. The techniques
are applied to the prediction of loudness levels corresponding to dynamic markings, and to
the classification of dynamic markings given loudness values. The results show that loudness
values and markings can be predicted relatively well when trained across recordings of the
same piece, but fail dismally when trained across a pianist's recordings of other pieces. The
findings demonstrate that score features may trump individual style when modelling loudness
choices. The analysis of the results reveal that form|such as the return of the theme - and
structure - such as repetitions -influence predictability of loudness and markings.
This research is a first step towards automatic audio-to-score transcription of dynamic
markings. This insight will serve as a tool for expression synthesis and musicological studies.||en_US
|dc.description.sponsorship||Queen Mary University of London||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||Expressive Music Performance||en_US
|dc.title||Computational Modelling and Quantitative Analysis of Dynamics in Performed Music||en_US