Performance analysis and analysis for performance : a study of Villa-Lobos's Bachianas Brasileiras No. 9

This study of Villa-Lobos’s Bachianas Brasileiras No. 9 aims at gaining insight into the decision-making processes of translating a score into a musical performance. Chapter I presents a discussion of selected issues related to interpretative analysis. Chapter II is an overview of the approaches to recording comparison deemed relevant to the present study. Chapter III is a comparative study of the vocal and string versions of Bachianas Brasileiras No. 9, while Chapter IV offers a structural analysis of the work. Chapter V compares four recordings: the composer’s own with the Orchestre National de La Radiodiffusion Française—EMI 7243 5 66964 2 6; Odaline de la Martinez and the BBC Singers, LNT 102; Michael Tilson Thomas and the New World Symphony —RCA 09026-68538-2; and my own CD, Construção, Orquestra de Câmara Theatro São Pedro- Limited Edition (live recording made on December 11, 1995 in Bayreuth, Germany). This comparison utilizes data obtained with the software Tempo. The tabulation of these results is shown in graphs that compare how matters of tempo flexibility affect each performance. This multi-faceted study shows that although painstaking analysis can lead to insightful solutions, the fleeting nature of musical performance requires an open mind and imagination to deal with the often contradictory directives of the score.

Graphical models and point set matching

Point pattern matching in Euclidean Spaces is one of the fundamental problems in Pattern Recognition, having applications ranging from Computer Vision to Computational Chemistry. Whenever two complex patterns are encoded by two sets of points identifying their key features, their comparison can be seen as a point pattern matching problem. This work proposes a single approach to both exact and inexact point set matching in Euclidean Spaces of arbitrary dimension. In the case of exact matching, it is assured to find an optimal solution. For inexact matching (when noise is involved), experimental results confirm the validity of the approach. We start by regarding point pattern matching as a weighted graph matching problem. We then formulate the weighted graph matching problem as one of Bayesian inference in a probabilistic graphical model. By exploiting the existence of fundamental constraints in patterns embedded in Euclidean Spaces, we prove that for exact point set matching a simple graphical model is equivalent to the full model. It is possible to show that exact probabilistic inference in this simple model has polynomial time complexity with respect to the number of elements in the patterns to be matched. This gives rise to a technique that for exact matching provably finds a global optimum in polynomial time for any dimensionality of the underlying Euclidean Space. Computational experiments comparing this technique with well-known probabilistic relaxation labeling show significant performance improvement for inexact matching. The proposed approach is significantly more robust under augmentation of the sizes of the involved patterns. In the absence of noise, the results are always perfect.