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This book presents analyses of pattern in music from different computational and mathematical perspectives. A central purpose of music analysis is to represent, discover, and evaluate repeated structures within single pieces or within larger corpora of related pieces. In the chapters of this book, music corpora are structured as monophonic melodies, polyphony, or chord sequences. Patterns are represented either extensionally as locations of pattern occurrences in the music, or intensionally as sequences of pitch or chord features, rhythmic profiles, geometric point sets, and logical expressions. The chapters cover both deductive analysis, where music is queried for occurrences of a known pattern, and inductive analysis, where patterns are found using pattern discovery algorithms. Results are evaluated using a variety of methods including visualization, contrasting corpus analysis, and reference to known and expected patterns. Pattern in Music will be a key resource for academics, researchers, and advanced students of music, musicology, music analyses, mathematical music theory, computational musicology, and music informatics. This book was originally published as a special issue of the Journal of Mathematics and Music.
Darrell Conklin is an Ikerbasque Research Professor in the Department of Computer Science and Artificial Intelligence at the University of the Basque Country, San Sebastian, Spain.
Introduction: Pattern in music 1. Discovering distorted repeating patterns in polyphonic music through longest increasing subsequences 2. Mining contour sequences for significant closed patterns 3. Parsimonious graphs for the most common trichords and tetrachords 4. Triadic patterns across classical and popular music corpora: stylistic conventions, or characteristic idioms? 5. Modelling pattern interestingness in comparative music corpus analysis 6. A computational exploration of melodic patterns in Arab-Andalusian music 7. Some observations on autocorrelated patterns within computational meter identification 8. Exploring annotations for musical pattern discovery gathered with digital annotation tools
