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Information Structures in Notated Music: Statistical Explorations of Composers' Performance Marks in Solo Piano Scores

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Information Structures in Notated Music: Statistical Explorations of Composers' Performance Marks in Solo Piano Scores INFORMATION STRUCTURES IN NOTATED MUSIC: STATISTICAL EXPLORATIONS OF COMPOSERS' PERFORMANCE MARKS IN SOLO PIANO SCORES J. Paul Buchanan Dissertation Prepared for the Degree of DOCTOR OF PHILOSOPHY UNIVERSITY OF NORTH TEXAS May 2016 APPROVED: Brian C. O'Connor, Major Professor Richard L. Anderson, Committee Member Maurice B. Wheeler, Committee Member Suliman Hawamdeh, Chair of the Department of Library and Information Sciences Greg Jones, Interim Dean of the College of Information Costas Tsatsoulis, Dean of the Toulouse Graduate School Buchanan, J. Paul. Information Structures in Notated Music: Statistical Explorations of Composers' Performance Marks in Solo Piano Scores. Doctor of Philosophy (Information Science), May 2016, 130 pp., 6 tables, 16 figures, references, 130 titles. Written notation has a long history in many musical traditions and has been particularly important in the composition and performance of Western art music. This study adopted the conceptual view that a musical score consists of two coordinated but separate communication channels: the musical text and a collection of composer- selected performance marks that serve as an interpretive gloss on that text. Structurally, these channels are defined by largely disjoint vocabularies of symbols and words. While the sound structures represented by musical texts are well studied in music theory and analysis, the stylistic patterns of performance marks and how they acquire contextual meaning in performance is an area with fewer theoretical foundations. This quantitative research explored the possibility that composers exhibit recurring patterns in their use of performance marks. Seventeen solo piano sonatas written between 1798 and 1913 by five major composers were analyzed from modern editions by tokenizing and tabulating the types and usage frequencies of their individual performance marks without regard to the associated musical texts. Using analytic methods common in information science, the results demonstrated persistent statistical similarities among the works of each composer and differences among the work groups of different composers. Although based on a small sample, the results still offered statistical support for the existence of recurring stylistic patterns in composers' use of performance marks across their works. Copyright 2016 by J. Paul Buchanan ii ACKNOWLEDGEMENTS The concepts and methods described here for incorporating studies of musical scores into the broader field of information science developed slowly and with the assistance of many people. I want to acknowledge all of the instructors during my doctoral coursework for exposing me to the many perspectives in contemporary information studies. I want to add a special acknowledgement to Mark McKnight and Andrew Justice for allowing me to serve as a research assistant in the UNT Music Library, where I came to appreciate the many contributions that music librarianship makes to the organization and preservation of music materials in all their formats. I am grateful to Richard Anderson and Maurice Wheeler for serving as members of my committee and offering so much assistance with both the substance and the process of this work. Rich's work with filmic documents was a particular inspiration to me, both conceptually and methodologically. I am immensely grateful to Brian O'Connor, my committee chair and mentor, for allowing my scholarly interests to find an intellectual home with him and for his years of guidance in the pursuit of them. At every stage, he has been able to help me focus, clarify, and articulate this research. Steve Buchanan, my brother and the serious musician in my family, spent hours discussing questions about the interpretation and performance of piano scores and reviewing my findings and conclusions as the research progressed. I am very thankful for his time and interest. Finally, my love and gratitude go to my wife Barbara, whose patience and practical encouragement enabled me to pursue this winding and sometimes quixotic journey so late in my career. iii TABLE OF CONTENTS Page ACKNOWLEDGEMENTS ................................................................................................ iii LIST OF TABLES ............................................................................................................ vi LIST OF FIGURES .......................................................................................................... vii CHAPTER 1. A MUSICAL INTRODUCTION ................................................................... 1 1.1 Encounters with Musical Scores ................................................................. 1 1.2 Scores and Their Performances ................................................................. 5 1.3 Musical Texts and Performance Annotations ........................................... 13 CHAPTER 2. MEASURING MUSICAL SCORES .......................................................... 19 2.1 Empirical Studies of Performance Marks ................................................. 19 2.2 Musical Scores as Documents ................................................................. 27 2.3 Structural Document Analysis .................................................................. 33 2.3.1 Zipf's Law ....................................................................................... 36 2.3.2 Information Entropy ....................................................................... 38 2.3.3 Vector Space Modeling .................................................................. 41 CHAPTER 3. RESEARCH QUESTION AND METHODS .............................................. 43 3.1 Research Question ................................................................................... 43 3.2 Score Corpus for Analysis ........................................................................ 43 3.3 Methods for Data Collection ..................................................................... 49 3.4 Methods for Statistical Analysis ................................................................ 53 3.4.1 Summary Statistics ........................................................................ 54 3.4.2 Score Cluster Analysis ................................................................... 55 iv CHAPTER 4. STATISTICAL FINDINGS AND ANALYSIS ............................................. 62 4.1 Corpus-Level Statistics ............................................................................. 62 4.2 Score-Level Summary Statistics ............................................................... 63 4.3 Score-Level Clustering Behavior .............................................................. 65 CHAPTER 5. CONCLUSION AND FUTURE DIRECTIONS .......................................... 69 5.1 Observations and Caveats ....................................................................... 69 5.2 Directions for Further Research ............................................................... 70 APPENDIX. CODING GUIDE ........................................................................................ 95 REFERENCES ............................................................................................................. 121 v LIST OF TABLES Page Table 1 Scores Coded for the Study ..................................................................... 73 Table 2 Ten Most Frequently Occuring Token Types in Corpus ........................... 74 Table 3 Token Types Occuring at Least Once in Every Score .............................. 75 Table 4 Score-Level Summary Statistics ............................................................... 76 Table 5 Evaluation Indices for Hierarchical Clustering .......................................... 77 Table 6 K-Means Clustering for Selected Weighting Functions ............................ 78 vi LIST OF FIGURES Page FIgure 1 Neumatic notation from the Cantatorium of St. Gall ................................. 79 Figure 2 Opening measures of Debussy's Réverie ................................................ 80 Figure 3 Final cadence of Skryabin's "Prelude" from Morceaux, Op. 51 ................ 81 Figure 4 Identifying ties and slurs from note contexts ............................................ 82 Figure 5 Performance annotation variation in Schumann's "Träumerei" ................ 83 Figure 6 Is the term agitato really a dynamic mark? ............................................... 84 Figure 7 Gradual dynamic terms with and without explicit extents ......................... 85 Figure 8 Apparently redundant and contradictory performance marks ................... 86 Figure 9 Examples of coded performance marks in Liszt's Piano Sonata .............. 87 Figure 10 Rank-frequency distribution and Zipf prediction for corpus ...................... 88 Figure 11 ANOVA plot of Zipf slopes for scores grouped by composer ................... 89 Figure 12 ANOVA plot of densities for scores grouped by composer ...................... 90 Figure 13 Linear regression plot of Zipf slopes for scores by date ........................... 91 Figure 14 Linear regression plot of densities for scores by date .............................. 92 Figure 15 Dendrogram for hierarchical clustering (tp-log-tp weights) ....................... 93 Figure 16 Dendrogram for hierarchical clustering (tp row-entropy weights) ............. 94 vii CHAPTER 1 A MUSICAL INTRODUCTION Written notation has a long history in many musical traditions and has been particularly important in the composition and performance of Western art music. Musical scores represent
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